Inventry_Management_Model.ipynb

{
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   "source": [
    "#Importing the libraries\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: scikit-learnNote: you may need to restart the kernel to use updated packages.\n",
      "Version: 0.23.0\n",
      "Summary: A set of python modules for machine learning and data mining\n",
      "Home-page: http://scikit-learn.org\n",
      "Author: None\n",
      "Author-email: None\n",
      "License: new BSD\n",
      "Location: c:\\users\\chinmaya\\anaconda3\\lib\\site-packages\n",
      "Requires: scipy, numpy, threadpoolctl, joblib\n",
      "Required-by: pmdarima, mlxtend, lightgbm, imbalanced-learn\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pip show scikit-learn"
   ]
  },
  {
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    "id": "bDSJwQM2uByz",
    "outputId": "e0584f01-287b-4b84-d9fd-26bfac4e7ef8"
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       "        year  month_no  week_no  Product_type  Revenue  Promotion_applied  \\\n",
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   "source": [
    "#Importing the dataset\n",
    "#prod = pd.read_csv('/content/drive/MyDrive/Deep Learning/Inventry Management/productdetails.csv')\n",
    "final = pd.read_csv('../final.csv',index_col=0)\n",
    "final"
   ]
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       "        year  month_no  week_no  Product_type  Revenue  Promotion_applied  \\\n",
       "291        1         1        1           292        0                  5   \n",
       "875        1         1        1           876        0                  5   \n",
       "1406       1         1        2           292        0                  0   \n",
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       "...      ...       ...      ...           ...      ...                ...   \n",
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       "145671     3        34      135           762        0                  0   \n",
       "145990     3        34      135          1081        0                  0   \n",
       "\n",
       "        Generic_Holiday  Education_Holiday  no_of_units  updated_week  \\\n",
       "291                   0                  5            0             1   \n",
       "875                   0                  5            0             1   \n",
       "1406                  0                  5            0             2   \n",
       "1990                  0                  5            0             2   \n",
       "2521                  0                  2            0             3   \n",
       "...                 ...                ...          ...           ...   \n",
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       "\n",
       "        updated_month  quarter  \n",
       "291                 1        1  \n",
       "875                 1        1  \n",
       "1406                1        1  \n",
       "1990                1        1  \n",
       "2521                1        1  \n",
       "...               ...      ...  \n",
       "145012             10        4  \n",
       "145248             10        4  \n",
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       "145671             10        4  \n",
       "145990             10        4  \n",
       "\n",
       "[216 rows x 12 columns]"
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     "execution_count": 4,
     "metadata": {},
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   "source": [
    "final[final.no_of_units==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Education_Holiday</th>\n",
       "      <th>no_of_units</th>\n",
       "      <th>updated_week</th>\n",
       "      <th>updated_month</th>\n",
       "      <th>quarter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>292</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1406</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>292</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2521</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>292</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3636</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>292</td>\n",
       "      <td>16672</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4751</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>292</td>\n",
       "      <td>84644</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>190</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140741</th>\n",
       "      <td>3</td>\n",
       "      <td>33</td>\n",
       "      <td>131</td>\n",
       "      <td>292</td>\n",
       "      <td>29124</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141856</th>\n",
       "      <td>3</td>\n",
       "      <td>33</td>\n",
       "      <td>132</td>\n",
       "      <td>292</td>\n",
       "      <td>35099</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>77</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142971</th>\n",
       "      <td>3</td>\n",
       "      <td>34</td>\n",
       "      <td>133</td>\n",
       "      <td>292</td>\n",
       "      <td>28069</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144086</th>\n",
       "      <td>3</td>\n",
       "      <td>34</td>\n",
       "      <td>134</td>\n",
       "      <td>292</td>\n",
       "      <td>37823</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>84</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145201</th>\n",
       "      <td>3</td>\n",
       "      <td>34</td>\n",
       "      <td>135</td>\n",
       "      <td>292</td>\n",
       "      <td>13858</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>135 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        year  month_no  week_no  Product_type  Revenue  Promotion_applied  \\\n",
       "291        1         1        1           292        0                  5   \n",
       "1406       1         1        2           292        0                  0   \n",
       "2521       1         1        3           292        0                  5   \n",
       "3636       1         1        4           292    16672                  0   \n",
       "4751       1         2        5           292    84644                  5   \n",
       "...      ...       ...      ...           ...      ...                ...   \n",
       "140741     3        33      131           292    29124                  0   \n",
       "141856     3        33      132           292    35099                  5   \n",
       "142971     3        34      133           292    28069                  0   \n",
       "144086     3        34      134           292    37823                  5   \n",
       "145201     3        34      135           292    13858                  0   \n",
       "\n",
       "        Generic_Holiday  Education_Holiday  no_of_units  updated_week  \\\n",
       "291                   0                  5            0             1   \n",
       "1406                  0                  5            0             2   \n",
       "2521                  0                  2            0             3   \n",
       "3636                  0                  0           35             4   \n",
       "4751                  0                  0          190             1   \n",
       "...                 ...                ...          ...           ...   \n",
       "140741                0                  0           62             3   \n",
       "141856                0                  0           77             4   \n",
       "142971                0                  0           61             1   \n",
       "144086                0                  0           84             2   \n",
       "145201                1                  4           30             3   \n",
       "\n",
       "        updated_month  quarter  \n",
       "291                 1        1  \n",
       "1406                1        1  \n",
       "2521                1        1  \n",
       "3636                1        1  \n",
       "4751                2        1  \n",
       "...               ...      ...  \n",
       "140741              9        3  \n",
       "141856              9        3  \n",
       "142971             10        4  \n",
       "144086             10        4  \n",
       "145201             10        4  \n",
       "\n",
       "[135 rows x 12 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final[final.Product_type==292]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████| 146025/146025 [00:11<00:00, 12833.97it/s]\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "for i in tqdm(range(0,len(final))):\n",
    "    if (final.no_of_units[i]==0):\n",
    "        final.no_of_units[i] = final.no_of_units[final.Product_type==final.Product_type[i]].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<p>146025 rows × 12 columns</p>\n",
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      ],
      "text/plain": [
       "        year  month_no  week_no  Product_type  Revenue  Promotion_applied  \\\n",
       "0          1         1        1             1    39469                  5   \n",
       "1          1         1        1             2    32843                  5   \n",
       "2          1         1        1             3    51626                  5   \n",
       "3          1         1        1             4    66305                  5   \n",
       "4          1         1        1             5    30836                  5   \n",
       "...      ...       ...      ...           ...      ...                ...   \n",
       "146020     3        34      135          1111    14316                  0   \n",
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       "146023     3        34      135          1114    57476                  0   \n",
       "146024     3        34      135          1115    12534                  0   \n",
       "\n",
       "        Generic_Holiday  Education_Holiday  no_of_units  updated_week  \\\n",
       "0                     0                  5           83             1   \n",
       "1                     0                  5           18             1   \n",
       "2                     0                  5           24             1   \n",
       "3                     0                  5          504             1   \n",
       "4                     0                  5           33             1   \n",
       "...                 ...                ...          ...           ...   \n",
       "146020                1                  4           10             3   \n",
       "146021                1                  4           30             3   \n",
       "146022                1                  4           41             3   \n",
       "146023                1                  4           44             3   \n",
       "146024                1                  4           27             3   \n",
       "\n",
       "        updated_month  quarter  \n",
       "0                   1        1  \n",
       "1                   1        1  \n",
       "2                   1        1  \n",
       "3                   1        1  \n",
       "4                   1        1  \n",
       "...               ...      ...  \n",
       "146020             10        4  \n",
       "146021             10        4  \n",
       "146022             10        4  \n",
       "146023             10        4  \n",
       "146024             10        4  \n",
       "\n",
       "[146025 rows x 12 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4500"
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     "execution_count": 8,
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  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "id": "j4P0oma9_DDP",
    "outputId": "a49ad686-5b18-499d-a617-90a91517da82"
   },
   "outputs": [
    {
     "data": {
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       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>66305</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>504</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>30836</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146020</th>\n",
       "      <td>3</td>\n",
       "      <td>1111</td>\n",
       "      <td>14316</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146021</th>\n",
       "      <td>3</td>\n",
       "      <td>1112</td>\n",
       "      <td>29301</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146022</th>\n",
       "      <td>3</td>\n",
       "      <td>1113</td>\n",
       "      <td>17305</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>41</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146023</th>\n",
       "      <td>3</td>\n",
       "      <td>1114</td>\n",
       "      <td>57476</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>44</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146024</th>\n",
       "      <td>3</td>\n",
       "      <td>1115</td>\n",
       "      <td>12534</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>27</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>146025 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        year  Product_type  Revenue  Promotion_applied  Generic_Holiday  \\\n",
       "0          1             1    39469                  5                0   \n",
       "1          1             2    32843                  5                0   \n",
       "2          1             3    51626                  5                0   \n",
       "3          1             4    66305                  5                0   \n",
       "4          1             5    30836                  5                0   \n",
       "...      ...           ...      ...                ...              ...   \n",
       "146020     3          1111    14316                  0                1   \n",
       "146021     3          1112    29301                  0                1   \n",
       "146022     3          1113    17305                  0                1   \n",
       "146023     3          1114    57476                  0                1   \n",
       "146024     3          1115    12534                  0                1   \n",
       "\n",
       "        Education_Holiday  no_of_units  updated_week  updated_month  \n",
       "0                       5           83             1              1  \n",
       "1                       5           18             1              1  \n",
       "2                       5           24             1              1  \n",
       "3                       5          504             1              1  \n",
       "4                       5           33             1              1  \n",
       "...                   ...          ...           ...            ...  \n",
       "146020                  4           10             3             10  \n",
       "146021                  4           30             3             10  \n",
       "146022                  4           41             3             10  \n",
       "146023                  4           44             3             10  \n",
       "146024                  4           27             3             10  \n",
       "\n",
       "[146025 rows x 9 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating weekly dataset\n",
    "weekly = final.drop(['month_no','week_no','quarter'],axis=1)\n",
    "weekly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "id": "h_3kUJry_i_9",
    "outputId": "c41687ab-99df-41e0-a853-73c4f876a053"
   },
   "outputs": [
    {
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       "      <td>4</td>\n",
       "      <td>175</td>\n",
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       "    <tr>\n",
       "      <th>36827</th>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>1113</td>\n",
       "      <td>89124</td>\n",
       "      <td>5</td>\n",
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       "      <td>4</td>\n",
       "      <td>209</td>\n",
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       "      <th>36828</th>\n",
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      "text/plain": [
       "       year  updated_month  Product_type  Revenue  Promotion_applied  \\\n",
       "0         1              1             1   114747                 10   \n",
       "1         1              1             2   116307                 10   \n",
       "2         1              1             3   165501                 10   \n",
       "3         1              1             4   243853                 10   \n",
       "4         1              1             5   107427                 10   \n",
       "...     ...            ...           ...      ...                ...   \n",
       "36825     3             10          1111    74600                  5   \n",
       "36826     3             10          1112   175491                  5   \n",
       "36827     3             10          1113    89124                  5   \n",
       "36828     3             10          1114   262661                  5   \n",
       "36829     3             10          1115    65824                  5   \n",
       "\n",
       "       Generic_Holiday  Education_Holiday  no_of_units  \n",
       "0                    0                  5          239  \n",
       "1                    0                 15           61  \n",
       "2                    0                 20           75  \n",
       "3                    0                 12         1852  \n",
       "4                    0                 15          113  \n",
       "...                ...                ...          ...  \n",
       "36825                1                  4           51  \n",
       "36826                1                  4          175  \n",
       "36827                1                  4          209  \n",
       "36828                1                  4          205  \n",
       "36829                1                 11          138  \n",
       "\n",
       "[36830 rows x 8 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly = final.groupby(['year','updated_month','Product_type']).sum().reset_index()\n",
    "monthly.drop(['month_no','week_no','quarter','updated_week'],axis=1,inplace=True)\n",
    "monthly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "id": "ZrAaHFig_9p5",
    "outputId": "edb25d97-d50a-4aad-dedd-35c43f89b4e7"
   },
   "outputs": [
    {
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       "      <td>4</td>\n",
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       "      <th>13198</th>\n",
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       "      <td>138</td>\n",
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       "</table>\n",
       "<p>13200 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       year  quarter  Product_type  Revenue  Promotion_applied  \\\n",
       "0         1        1             1   315247                 30   \n",
       "1         1        1             2   347640                 30   \n",
       "2         1        1             3   492968                 30   \n",
       "3         1        1             4   693969                 30   \n",
       "4         1        1             5   323434                 30   \n",
       "...     ...      ...           ...      ...                ...   \n",
       "13195     3        4          1111    74600                  5   \n",
       "13196     3        4          1112   175491                  5   \n",
       "13197     3        4          1113    89124                  5   \n",
       "13198     3        4          1114   262661                  5   \n",
       "13199     3        4          1115    65824                  5   \n",
       "\n",
       "       Generic_Holiday  Education_Holiday  no_of_units  \n",
       "0                    3                  5          654  \n",
       "1                    2                 16          189  \n",
       "2                    3                 26          227  \n",
       "3                    2                 14         5265  \n",
       "4                    3                 16          337  \n",
       "...                ...                ...          ...  \n",
       "13195                1                  4           51  \n",
       "13196                1                  4          175  \n",
       "13197                1                  4          209  \n",
       "13198                1                  4          205  \n",
       "13199                1                 11          138  \n",
       "\n",
       "[13200 rows x 8 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quarter = final.groupby(['year','quarter','Product_type']).sum().reset_index()\n",
    "quarter.drop(['month_no','week_no','updated_week','updated_month'],axis=1,inplace=True)\n",
    "quarter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>83</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>32843</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>51626</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>66305</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>504</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>30836</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146020</th>\n",
       "      <td>3</td>\n",
       "      <td>1111</td>\n",
       "      <td>14316</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146021</th>\n",
       "      <td>3</td>\n",
       "      <td>1112</td>\n",
       "      <td>29301</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146022</th>\n",
       "      <td>3</td>\n",
       "      <td>1113</td>\n",
       "      <td>17305</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>41</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146023</th>\n",
       "      <td>3</td>\n",
       "      <td>1114</td>\n",
       "      <td>57476</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>44</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146024</th>\n",
       "      <td>3</td>\n",
       "      <td>1115</td>\n",
       "      <td>12534</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>27</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>146025 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        year  Product_type  Revenue  Promotion_applied  Generic_Holiday  \\\n",
       "0          1             1    39469                  5                0   \n",
       "1          1             2    32843                  5                0   \n",
       "2          1             3    51626                  5                0   \n",
       "3          1             4    66305                  5                0   \n",
       "4          1             5    30836                  5                0   \n",
       "...      ...           ...      ...                ...              ...   \n",
       "146020     3          1111    14316                  0                1   \n",
       "146021     3          1112    29301                  0                1   \n",
       "146022     3          1113    17305                  0                1   \n",
       "146023     3          1114    57476                  0                1   \n",
       "146024     3          1115    12534                  0                1   \n",
       "\n",
       "        Education_Holiday  no_of_units  updated_week  updated_month  \n",
       "0                       5           83             1              1  \n",
       "1                       5           18             1              1  \n",
       "2                       5           24             1              1  \n",
       "3                       5          504             1              1  \n",
       "4                       5           33             1              1  \n",
       "...                   ...          ...           ...            ...  \n",
       "146020                  4           10             3             10  \n",
       "146021                  4           30             3             10  \n",
       "146022                  4           41             3             10  \n",
       "146023                  4           44             3             10  \n",
       "146024                  4           27             3             10  \n",
       "\n",
       "[146025 rows x 9 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weekly"
   ]
  },
  {
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    "id": "aaNKdpCZAGsd",
    "outputId": "c3604daa-51d8-48ea-8c26-9b2805699c3d"
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       "      <td>281.0</td>\n",
       "      <td>558.0</td>\n",
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       "      <td>50460.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>837.0</td>\n",
       "      <td>1115.0</td>\n",
       "      <td>52080.0</td>\n",
       "      <td>40329.964785</td>\n",
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       "      <td>3.25</td>\n",
       "      <td>4.0</td>\n",
       "      <td>52080.0</td>\n",
       "      <td>6.638249</td>\n",
       "      <td>3.395455</td>\n",
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       "      <td>321.876347</td>\n",
       "      <td>1.0</td>\n",
       "      <td>279.0</td>\n",
       "      <td>558.0</td>\n",
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       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
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      "text/plain": [
       "     Product_type                                                            \\\n",
       "            count        mean         std  min    25%    50%    75%     max   \n",
       "year                                                                          \n",
       "1         50460.0  558.801149  321.942366  1.0  281.0  558.0  838.0  1115.0   \n",
       "2         52080.0  558.365284  321.906320  1.0  280.0  558.0  837.0  1115.0   \n",
       "3         43485.0  558.000000  321.876347  1.0  279.0  558.0  837.0  1115.0   \n",
       "\n",
       "      Revenue                ... updated_week      updated_month            \\\n",
       "        count          mean  ...          75%  max         count      mean   \n",
       "year                         ...                                             \n",
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       "3     43485.0  38477.575463  ...         3.00  4.0       43485.0  5.384615   \n",
       "\n",
       "                                           \n",
       "           std  min  25%  50%   75%   max  \n",
       "year                                       \n",
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       "2     3.395455  1.0  4.0  7.0  10.0  12.0  \n",
       "3     2.815879  1.0  3.0  5.0   8.0  10.0  \n",
       "\n",
       "[3 rows x 64 columns]"
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     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "weekly.groupby('year').describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
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    "id": "HbOUfZxACuPH",
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       "      <td>0.798442</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>26950.0</td>\n",
       "      <td>559.451911</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>26950.0</td>\n",
       "      <td>6.212876</td>\n",
       "      <td>2.721434</td>\n",
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       "      <td>4.0</td>\n",
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       "      <td>2050.0</td>\n",
       "      <td>1.543902</td>\n",
       "      <td>0.498190</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
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      "text/plain": [
       "                     year                                               \\\n",
       "                    count      mean       std  min  25%  50%  75%  max   \n",
       "Generic_Holiday                                                          \n",
       "0                117025.0  1.960017  0.803616  1.0  1.0  2.0  3.0  3.0   \n",
       "1                 26950.0  1.949499  0.798442  1.0  1.0  2.0  3.0  3.0   \n",
       "2                  2050.0  1.543902  0.498190  1.0  1.0  2.0  2.0  2.0   \n",
       "\n",
       "                Product_type              ... updated_week      updated_month  \\\n",
       "                       count        mean  ...          75%  max         count   \n",
       "Generic_Holiday                           ...                                   \n",
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       "2                     2050.0  559.160000  ...          4.0  4.0        2050.0   \n",
       "\n",
       "                                                               \n",
       "                     mean       std  min  25%  50%  75%   max  \n",
       "Generic_Holiday                                                \n",
       "0                6.076633  3.404206  1.0  3.0  6.0  9.0  12.0  \n",
       "1                6.212876  2.721434  2.0  4.0  5.0  8.0  12.0  \n",
       "2                8.543902  0.498190  8.0  8.0  9.0  9.0   9.0  \n",
       "\n",
       "[3 rows x 64 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weekly.groupby('Generic_Holiday').describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 450
    },
    "id": "S3hm1y-aJpbG",
    "outputId": "97ecd3d8-0ec6-4ef5-c49d-aad390af490f"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>year</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product_type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2746</td>\n",
       "      <td>2732</td>\n",
       "      <td>2283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>775</td>\n",
       "      <td>748</td>\n",
       "      <td>584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>881</td>\n",
       "      <td>858</td>\n",
       "      <td>707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21176</td>\n",
       "      <td>20326</td>\n",
       "      <td>15793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1389</td>\n",
       "      <td>1359</td>\n",
       "      <td>1077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1111</th>\n",
       "      <td>959</td>\n",
       "      <td>1052</td>\n",
       "      <td>822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1112</th>\n",
       "      <td>2512</td>\n",
       "      <td>2732</td>\n",
       "      <td>2611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1113</th>\n",
       "      <td>4489</td>\n",
       "      <td>4393</td>\n",
       "      <td>3332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1114</th>\n",
       "      <td>4720</td>\n",
       "      <td>4526</td>\n",
       "      <td>3514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1115</th>\n",
       "      <td>4207</td>\n",
       "      <td>3715</td>\n",
       "      <td>2537</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1115 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "year              1      2      3\n",
       "Product_type                     \n",
       "1              2746   2732   2283\n",
       "2               775    748    584\n",
       "3               881    858    707\n",
       "4             21176  20326  15793\n",
       "5              1389   1359   1077\n",
       "...             ...    ...    ...\n",
       "1111            959   1052    822\n",
       "1112           2512   2732   2611\n",
       "1113           4489   4393   3332\n",
       "1114           4720   4526   3514\n",
       "1115           4207   3715   2537\n",
       "\n",
       "[1115 rows x 3 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating dataframe of product type , year and no. of units\n",
    "pd.crosstab(index=weekly.Product_type,columns=weekly.year,values=weekly.no_of_units,aggfunc='sum')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GfK8bLSPKVnf",
    "outputId": "11e12219-d011-4bed-b051-5dcf1f95dbde"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    19909\n",
       "1     7125\n",
       "2     5901\n",
       "3     3457\n",
       "4      438\n",
       "Name: Generic_Holiday, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly.Generic_Holiday.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "AFEx7MkZK9Us",
    "outputId": "59bbe1aa-8d4e-40b6-e79e-6ccd4db44a19"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    3956\n",
       "2    3917\n",
       "4    2319\n",
       "1    2072\n",
       "0     936\n",
       "Name: Generic_Holiday, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quarter.Generic_Holiday.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 369
    },
    "id": "5CKG5s-mLCl2",
    "outputId": "e855e1ea-00fd-49d9-b2c4-de948c3109d3"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualizing Revenue wrt week, month,quarter\n",
    "fig, axes = plt.subplots(ncols=3,nrows=1,tight_layout=True,figsize=(15,5))\n",
    "axes[0].set_title('Weekly')\n",
    "sns.boxplot(weekly.Revenue,ax=axes[0])\n",
    "axes[1].set_title('Monthly')\n",
    "sns.boxplot(monthly.Revenue,ax=axes[1])\n",
    "axes[2].set_title('Quarterly')\n",
    "sns.boxplot(quarter.Revenue,ax=axes[2])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 369
    },
    "id": "2mo7zZzuSEDI",
    "outputId": "adf9c7f4-b283-40dd-f8a3-bf2b20ff0539"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualizing no of units wrt week, month,quarter\n",
    "fig, axes = plt.subplots(ncols=3,nrows=1,tight_layout=True,figsize=(15,5))\n",
    "axes[0].set_title('Weekly')\n",
    "sns.boxplot(weekly.no_of_units,ax=axes[0])\n",
    "axes[1].set_title('Monthly')\n",
    "sns.boxplot(monthly.no_of_units,ax=axes[1])\n",
    "axes[2].set_title('Quarterly')\n",
    "sns.boxplot(quarter.no_of_units,ax=axes[2])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 369
    },
    "id": "OMvYc7R5UXVG",
    "outputId": "415360a9-0d53-44d7-b0d3-353f0853e2c4"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualizing Generic holiday wrt week, month,quarter\n",
    "fig, axes = plt.subplots(ncols=3,nrows=1,tight_layout=True,figsize=(15,5))\n",
    "axes[0].set_title('Weekly')\n",
    "sns.boxplot(x=weekly.Generic_Holiday,y=weekly.no_of_units,ax=axes[0])\n",
    "axes[1].set_title('Monthly')\n",
    "sns.boxplot(x=monthly.Generic_Holiday,y=monthly.no_of_units,ax=axes[1])\n",
    "axes[2].set_title('Quarterly')\n",
    "sns.boxplot(x=quarter.Generic_Holiday,y=quarter.no_of_units,ax=axes[2])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 369
    },
    "id": "_Kg6RTHRU6L7",
    "outputId": "8a566295-448f-4995-c751-7384b4b8d2d7"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualizing Promotion Applied wrt week, month,quarter\n",
    "fig, axes = plt.subplots(ncols=3,nrows=1,tight_layout=True,figsize=(15,5))\n",
    "axes[0].set_title('Weekly')\n",
    "sns.boxplot(x=weekly.Promotion_applied,y=weekly.no_of_units,ax=axes[0])\n",
    "axes[1].set_title('Monthly')\n",
    "sns.boxplot(x=monthly.Promotion_applied,y=monthly.no_of_units,ax=axes[1])\n",
    "axes[2].set_title('Quarterly')\n",
    "sns.boxplot(x=quarter.Promotion_applied,y=quarter.no_of_units,ax=axes[2])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 369
    },
    "id": "ao9e7wGfkzYG",
    "outputId": "f47b4c50-5851-41d8-c3c4-269555317eff"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualizing no of units wrt week, month,quarter\n",
    "fig, axes = plt.subplots(ncols=3,nrows=1,tight_layout=True,figsize=(15,5))\n",
    "axes[0].set_title('Weekly')\n",
    "sns.boxplot(x=weekly.Education_Holiday,y=weekly.no_of_units,ax=axes[0])\n",
    "axes[1].set_title('Monthly')\n",
    "sns.boxplot(x=monthly.Education_Holiday,y=monthly.no_of_units,ax=axes[1])\n",
    "axes[2].set_title('Quarterly')\n",
    "sns.boxplot(x=quarter.Education_Holiday,y=quarter.no_of_units,ax=axes[2])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "ME-NlE54VNGs",
    "outputId": "668cbca6-2699-44a0-feae-c07ea884e0db"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">Revenue</th>\n",
       "      <th colspan=\"8\" halign=\"left\">no_of_units</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Promotion_applied</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>68265.0</td>\n",
       "      <td>33729.456544</td>\n",
       "      <td>13606.873470</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25064.00</td>\n",
       "      <td>31518.0</td>\n",
       "      <td>39358.00</td>\n",
       "      <td>190357.0</td>\n",
       "      <td>68265.0</td>\n",
       "      <td>61.350297</td>\n",
       "      <td>97.302058</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>1520.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>180.0</td>\n",
       "      <td>17061.283333</td>\n",
       "      <td>5702.590806</td>\n",
       "      <td>8112.0</td>\n",
       "      <td>13698.75</td>\n",
       "      <td>15916.0</td>\n",
       "      <td>19578.25</td>\n",
       "      <td>45931.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>33.688889</td>\n",
       "      <td>52.092021</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>343.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>77580.0</td>\n",
       "      <td>45985.846004</td>\n",
       "      <td>16983.205576</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34805.75</td>\n",
       "      <td>43260.0</td>\n",
       "      <td>53455.25</td>\n",
       "      <td>183230.0</td>\n",
       "      <td>77580.0</td>\n",
       "      <td>84.087046</td>\n",
       "      <td>127.801757</td>\n",
       "      <td>1.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>2423.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Revenue                                                \\\n",
       "                     count          mean           std     min       25%   \n",
       "Promotion_applied                                                          \n",
       "0                  68265.0  33729.456544  13606.873470     0.0  25064.00   \n",
       "1                    180.0  17061.283333   5702.590806  8112.0  13698.75   \n",
       "5                  77580.0  45985.846004  16983.205576     0.0  34805.75   \n",
       "\n",
       "                                               no_of_units             \\\n",
       "                       50%       75%       max       count       mean   \n",
       "Promotion_applied                                                       \n",
       "0                  31518.0  39358.00  190357.0     68265.0  61.350297   \n",
       "1                  15916.0  19578.25   45931.0       180.0  33.688889   \n",
       "5                  43260.0  53455.25  183230.0     77580.0  84.087046   \n",
       "\n",
       "                                                              \n",
       "                          std  min   25%   50%   75%     max  \n",
       "Promotion_applied                                             \n",
       "0                   97.302058  1.0  18.0  30.0  63.0  1520.0  \n",
       "1                   52.092021  4.0  10.0  16.0  31.0   343.0  \n",
       "5                  127.801757  1.0  26.0  42.0  87.0  2423.0  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#promotion applied variable in weekly dataset\n",
    "weekly[['Promotion_applied','Revenue','no_of_units']].groupby('Promotion_applied').describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 255
    },
    "id": "b0mbHrFLVkYF",
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    {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">Revenue</th>\n",
       "      <th colspan=\"8\" halign=\"left\">no_of_units</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Promotion_applied</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2230.0</td>\n",
       "      <td>118798.167265</td>\n",
       "      <td>51922.120514</td>\n",
       "      <td>0.0</td>\n",
       "      <td>83921.50</td>\n",
       "      <td>109677.5</td>\n",
       "      <td>144037.25</td>\n",
       "      <td>588033.0</td>\n",
       "      <td>2230.0</td>\n",
       "      <td>217.498655</td>\n",
       "      <td>347.248416</td>\n",
       "      <td>8.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>221.00</td>\n",
       "      <td>4760.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>180.0</td>\n",
       "      <td>120408.894444</td>\n",
       "      <td>38491.569852</td>\n",
       "      <td>62801.0</td>\n",
       "      <td>94747.00</td>\n",
       "      <td>112784.5</td>\n",
       "      <td>136932.00</td>\n",
       "      <td>358450.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>236.700000</td>\n",
       "      <td>368.102198</td>\n",
       "      <td>26.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>227.25</td>\n",
       "      <td>2724.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>28090.0</td>\n",
       "      <td>160143.431684</td>\n",
       "      <td>59375.254491</td>\n",
       "      <td>0.0</td>\n",
       "      <td>121153.75</td>\n",
       "      <td>150989.5</td>\n",
       "      <td>185666.75</td>\n",
       "      <td>646111.0</td>\n",
       "      <td>28090.0</td>\n",
       "      <td>292.110253</td>\n",
       "      <td>449.324532</td>\n",
       "      <td>12.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>300.00</td>\n",
       "      <td>7296.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>6330.0</td>\n",
       "      <td>171907.078199</td>\n",
       "      <td>64530.484582</td>\n",
       "      <td>0.0</td>\n",
       "      <td>129054.00</td>\n",
       "      <td>161294.0</td>\n",
       "      <td>199962.75</td>\n",
       "      <td>659082.0</td>\n",
       "      <td>6330.0</td>\n",
       "      <td>313.524487</td>\n",
       "      <td>480.021407</td>\n",
       "      <td>18.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>323.00</td>\n",
       "      <td>7166.0</td>\n",
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      "text/plain": [
       "                   Revenue                                                   \\\n",
       "                     count           mean           std      min        25%   \n",
       "Promotion_applied                                                             \n",
       "5                   2230.0  118798.167265  51922.120514      0.0   83921.50   \n",
       "6                    180.0  120408.894444  38491.569852  62801.0   94747.00   \n",
       "10                 28090.0  160143.431684  59375.254491      0.0  121153.75   \n",
       "15                  6330.0  171907.078199  64530.484582      0.0  129054.00   \n",
       "\n",
       "                                                 no_of_units              \\\n",
       "                        50%        75%       max       count        mean   \n",
       "Promotion_applied                                                          \n",
       "5                  109677.5  144037.25  588033.0      2230.0  217.498655   \n",
       "6                  112784.5  136932.00  358450.0       180.0  236.700000   \n",
       "10                 150989.5  185666.75  646111.0     28090.0  292.110253   \n",
       "15                 161294.0  199962.75  659082.0      6330.0  313.524487   \n",
       "\n",
       "                                                                  \n",
       "                          std   min   25%    50%     75%     max  \n",
       "Promotion_applied                                                 \n",
       "5                  347.248416   8.0  64.0  108.0  221.00  4760.0  \n",
       "6                  368.102198  26.0  65.0  116.0  227.25  2724.0  \n",
       "10                 449.324532  12.0  89.0  145.0  300.00  7296.0  \n",
       "15                 480.021407  18.0  96.0  157.0  323.00  7166.0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#promotion applied variable in monthly dataset\n",
    "monthly[['Promotion_applied','Revenue','no_of_units']].groupby('Promotion_applied').describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
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     "height": 317
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    "id": "lLcZZ45rV2i-",
    "outputId": "cc150bd2-48e0-43f7-b77a-eb0c93de6cb2"
   },
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">Revenue</th>\n",
       "      <th colspan=\"8\" halign=\"left\">no_of_units</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Promotion_applied</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1115.0</td>\n",
       "      <td>92933.210762</td>\n",
       "      <td>36450.149861</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70201.5</td>\n",
       "      <td>87577.0</td>\n",
       "      <td>109263.00</td>\n",
       "      <td>353261.0</td>\n",
       "      <td>1115.0</td>\n",
       "      <td>170.168610</td>\n",
       "      <td>263.263779</td>\n",
       "      <td>8.0</td>\n",
       "      <td>50.50</td>\n",
       "      <td>85.0</td>\n",
       "      <td>175.00</td>\n",
       "      <td>2912.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>180.0</td>\n",
       "      <td>120408.894444</td>\n",
       "      <td>38491.569852</td>\n",
       "      <td>62801.0</td>\n",
       "      <td>94747.0</td>\n",
       "      <td>112784.5</td>\n",
       "      <td>136932.00</td>\n",
       "      <td>358450.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>236.700000</td>\n",
       "      <td>368.102198</td>\n",
       "      <td>26.0</td>\n",
       "      <td>65.00</td>\n",
       "      <td>116.0</td>\n",
       "      <td>227.25</td>\n",
       "      <td>2724.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>180.0</td>\n",
       "      <td>243049.494444</td>\n",
       "      <td>78094.831689</td>\n",
       "      <td>138174.0</td>\n",
       "      <td>192581.0</td>\n",
       "      <td>223500.0</td>\n",
       "      <td>271508.00</td>\n",
       "      <td>713835.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>479.911111</td>\n",
       "      <td>740.811893</td>\n",
       "      <td>55.0</td>\n",
       "      <td>129.75</td>\n",
       "      <td>233.0</td>\n",
       "      <td>449.75</td>\n",
       "      <td>5251.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1115.0</td>\n",
       "      <td>462690.565022</td>\n",
       "      <td>164433.548507</td>\n",
       "      <td>177027.0</td>\n",
       "      <td>356044.5</td>\n",
       "      <td>438723.0</td>\n",
       "      <td>531445.00</td>\n",
       "      <td>1679468.0</td>\n",
       "      <td>1115.0</td>\n",
       "      <td>844.890583</td>\n",
       "      <td>1291.924500</td>\n",
       "      <td>80.0</td>\n",
       "      <td>255.50</td>\n",
       "      <td>422.0</td>\n",
       "      <td>858.50</td>\n",
       "      <td>14158.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>4280.0</td>\n",
       "      <td>490976.529206</td>\n",
       "      <td>177727.851289</td>\n",
       "      <td>107199.0</td>\n",
       "      <td>373333.0</td>\n",
       "      <td>462523.5</td>\n",
       "      <td>569173.75</td>\n",
       "      <td>1808762.0</td>\n",
       "      <td>4280.0</td>\n",
       "      <td>894.763551</td>\n",
       "      <td>1371.494726</td>\n",
       "      <td>59.0</td>\n",
       "      <td>273.00</td>\n",
       "      <td>446.0</td>\n",
       "      <td>930.00</td>\n",
       "      <td>17271.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>6330.0</td>\n",
       "      <td>487657.464455</td>\n",
       "      <td>178189.380842</td>\n",
       "      <td>0.0</td>\n",
       "      <td>369965.5</td>\n",
       "      <td>461820.5</td>\n",
       "      <td>563568.25</td>\n",
       "      <td>1819495.0</td>\n",
       "      <td>6330.0</td>\n",
       "      <td>888.980411</td>\n",
       "      <td>1360.747888</td>\n",
       "      <td>57.0</td>\n",
       "      <td>272.00</td>\n",
       "      <td>444.0</td>\n",
       "      <td>919.00</td>\n",
       "      <td>17212.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                  Revenue                                                    \\\n",
       "                    count           mean            std       min       25%   \n",
       "Promotion_applied                                                             \n",
       "5                  1115.0   92933.210762   36450.149861       0.0   70201.5   \n",
       "6                   180.0  120408.894444   38491.569852   62801.0   94747.0   \n",
       "20                  180.0  243049.494444   78094.831689  138174.0  192581.0   \n",
       "25                 1115.0  462690.565022  164433.548507  177027.0  356044.5   \n",
       "30                 4280.0  490976.529206  177727.851289  107199.0  373333.0   \n",
       "35                 6330.0  487657.464455  178189.380842       0.0  369965.5   \n",
       "\n",
       "                                                  no_of_units              \\\n",
       "                        50%        75%        max       count        mean   \n",
       "Promotion_applied                                                           \n",
       "5                   87577.0  109263.00   353261.0      1115.0  170.168610   \n",
       "6                  112784.5  136932.00   358450.0       180.0  236.700000   \n",
       "20                 223500.0  271508.00   713835.0       180.0  479.911111   \n",
       "25                 438723.0  531445.00  1679468.0      1115.0  844.890583   \n",
       "30                 462523.5  569173.75  1808762.0      4280.0  894.763551   \n",
       "35                 461820.5  563568.25  1819495.0      6330.0  888.980411   \n",
       "\n",
       "                                                                      \n",
       "                           std   min     25%    50%     75%      max  \n",
       "Promotion_applied                                                     \n",
       "5                   263.263779   8.0   50.50   85.0  175.00   2912.0  \n",
       "6                   368.102198  26.0   65.00  116.0  227.25   2724.0  \n",
       "20                  740.811893  55.0  129.75  233.0  449.75   5251.0  \n",
       "25                 1291.924500  80.0  255.50  422.0  858.50  14158.0  \n",
       "30                 1371.494726  59.0  273.00  446.0  930.00  17271.0  \n",
       "35                 1360.747888  57.0  272.00  444.0  919.00  17212.0  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#promotion applied variable in quarter dataset\n",
    "quarter[['Promotion_applied','Revenue','no_of_units']].groupby('Promotion_applied').describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
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     "height": 782
    },
    "id": "xayZcvaXlHeB",
    "outputId": "42f7ab48-2139-4ba5-adb8-066c77037c1b"
   },
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>Education_Holiday</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>16354.0</td>\n",
       "      <td>164709.784395</td>\n",
       "      <td>61493.720897</td>\n",
       "      <td>0.0</td>\n",
       "      <td>124065.25</td>\n",
       "      <td>154352.0</td>\n",
       "      <td>191331.00</td>\n",
       "      <td>659082.0</td>\n",
       "      <td>16354.0</td>\n",
       "      <td>300.587991</td>\n",
       "      <td>456.582979</td>\n",
       "      <td>12.0</td>\n",
       "      <td>91.00</td>\n",
       "      <td>150.0</td>\n",
       "      <td>312.75</td>\n",
       "      <td>7296.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2068.0</td>\n",
       "      <td>163259.782882</td>\n",
       "      <td>62768.253719</td>\n",
       "      <td>0.0</td>\n",
       "      <td>123148.50</td>\n",
       "      <td>153307.0</td>\n",
       "      <td>188643.75</td>\n",
       "      <td>619935.0</td>\n",
       "      <td>2068.0</td>\n",
       "      <td>291.819632</td>\n",
       "      <td>450.671307</td>\n",
       "      <td>29.0</td>\n",
       "      <td>93.00</td>\n",
       "      <td>143.5</td>\n",
       "      <td>297.25</td>\n",
       "      <td>4846.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>737.0</td>\n",
       "      <td>160878.067843</td>\n",
       "      <td>69131.438003</td>\n",
       "      <td>29402.0</td>\n",
       "      <td>115893.00</td>\n",
       "      <td>149776.0</td>\n",
       "      <td>188163.00</td>\n",
       "      <td>646111.0</td>\n",
       "      <td>737.0</td>\n",
       "      <td>324.507463</td>\n",
       "      <td>558.060581</td>\n",
       "      <td>22.0</td>\n",
       "      <td>90.00</td>\n",
       "      <td>153.0</td>\n",
       "      <td>296.00</td>\n",
       "      <td>4612.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>565.0</td>\n",
       "      <td>132157.458407</td>\n",
       "      <td>51340.216441</td>\n",
       "      <td>50512.0</td>\n",
       "      <td>91984.00</td>\n",
       "      <td>124905.0</td>\n",
       "      <td>161863.00</td>\n",
       "      <td>433570.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>260.702655</td>\n",
       "      <td>437.836335</td>\n",
       "      <td>22.0</td>\n",
       "      <td>69.00</td>\n",
       "      <td>124.0</td>\n",
       "      <td>267.00</td>\n",
       "      <td>4396.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1348.0</td>\n",
       "      <td>112283.588279</td>\n",
       "      <td>49645.937981</td>\n",
       "      <td>0.0</td>\n",
       "      <td>77285.75</td>\n",
       "      <td>101223.0</td>\n",
       "      <td>135326.00</td>\n",
       "      <td>427571.0</td>\n",
       "      <td>1348.0</td>\n",
       "      <td>208.701780</td>\n",
       "      <td>329.739329</td>\n",
       "      <td>8.0</td>\n",
       "      <td>60.00</td>\n",
       "      <td>101.5</td>\n",
       "      <td>209.50</td>\n",
       "      <td>3295.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3402.0</td>\n",
       "      <td>160819.537625</td>\n",
       "      <td>59511.431043</td>\n",
       "      <td>0.0</td>\n",
       "      <td>121279.50</td>\n",
       "      <td>151293.5</td>\n",
       "      <td>186777.00</td>\n",
       "      <td>650798.0</td>\n",
       "      <td>3402.0</td>\n",
       "      <td>278.445326</td>\n",
       "      <td>425.227353</td>\n",
       "      <td>25.0</td>\n",
       "      <td>87.00</td>\n",
       "      <td>142.5</td>\n",
       "      <td>285.75</td>\n",
       "      <td>5210.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>318.0</td>\n",
       "      <td>152861.103774</td>\n",
       "      <td>50373.018489</td>\n",
       "      <td>0.0</td>\n",
       "      <td>120447.00</td>\n",
       "      <td>150006.0</td>\n",
       "      <td>183002.50</td>\n",
       "      <td>358845.0</td>\n",
       "      <td>318.0</td>\n",
       "      <td>256.930818</td>\n",
       "      <td>490.349050</td>\n",
       "      <td>35.0</td>\n",
       "      <td>86.00</td>\n",
       "      <td>127.0</td>\n",
       "      <td>258.75</td>\n",
       "      <td>5329.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>422.0</td>\n",
       "      <td>157708.246445</td>\n",
       "      <td>56935.759852</td>\n",
       "      <td>58486.0</td>\n",
       "      <td>125354.25</td>\n",
       "      <td>150233.5</td>\n",
       "      <td>177712.25</td>\n",
       "      <td>605400.0</td>\n",
       "      <td>422.0</td>\n",
       "      <td>313.784360</td>\n",
       "      <td>535.078020</td>\n",
       "      <td>35.0</td>\n",
       "      <td>86.25</td>\n",
       "      <td>145.5</td>\n",
       "      <td>304.00</td>\n",
       "      <td>5136.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>351.0</td>\n",
       "      <td>150517.170940</td>\n",
       "      <td>45880.078449</td>\n",
       "      <td>72653.0</td>\n",
       "      <td>119001.50</td>\n",
       "      <td>141580.0</td>\n",
       "      <td>180527.00</td>\n",
       "      <td>402029.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>295.977208</td>\n",
       "      <td>443.389928</td>\n",
       "      <td>32.0</td>\n",
       "      <td>85.00</td>\n",
       "      <td>139.0</td>\n",
       "      <td>313.50</td>\n",
       "      <td>3534.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>803.0</td>\n",
       "      <td>142876.703611</td>\n",
       "      <td>48064.566784</td>\n",
       "      <td>62801.0</td>\n",
       "      <td>109258.00</td>\n",
       "      <td>133907.0</td>\n",
       "      <td>166696.50</td>\n",
       "      <td>495786.0</td>\n",
       "      <td>803.0</td>\n",
       "      <td>311.260274</td>\n",
       "      <td>509.165888</td>\n",
       "      <td>26.0</td>\n",
       "      <td>80.50</td>\n",
       "      <td>145.0</td>\n",
       "      <td>298.00</td>\n",
       "      <td>4413.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4497.0</td>\n",
       "      <td>161025.534801</td>\n",
       "      <td>59507.827982</td>\n",
       "      <td>0.0</td>\n",
       "      <td>121298.00</td>\n",
       "      <td>151713.0</td>\n",
       "      <td>186302.00</td>\n",
       "      <td>588033.0</td>\n",
       "      <td>4497.0</td>\n",
       "      <td>289.724705</td>\n",
       "      <td>438.079835</td>\n",
       "      <td>18.0</td>\n",
       "      <td>90.00</td>\n",
       "      <td>147.0</td>\n",
       "      <td>299.00</td>\n",
       "      <td>5220.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>603.0</td>\n",
       "      <td>140894.966833</td>\n",
       "      <td>57462.132805</td>\n",
       "      <td>9334.0</td>\n",
       "      <td>102874.50</td>\n",
       "      <td>130289.0</td>\n",
       "      <td>169307.50</td>\n",
       "      <td>497997.0</td>\n",
       "      <td>603.0</td>\n",
       "      <td>275.900498</td>\n",
       "      <td>409.994194</td>\n",
       "      <td>11.0</td>\n",
       "      <td>77.00</td>\n",
       "      <td>136.0</td>\n",
       "      <td>298.00</td>\n",
       "      <td>3379.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1746.0</td>\n",
       "      <td>160743.247995</td>\n",
       "      <td>61455.488602</td>\n",
       "      <td>16672.0</td>\n",
       "      <td>121651.50</td>\n",
       "      <td>152021.0</td>\n",
       "      <td>185542.75</td>\n",
       "      <td>607398.0</td>\n",
       "      <td>1746.0</td>\n",
       "      <td>285.931844</td>\n",
       "      <td>427.782160</td>\n",
       "      <td>19.0</td>\n",
       "      <td>89.00</td>\n",
       "      <td>143.5</td>\n",
       "      <td>290.00</td>\n",
       "      <td>4704.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>337.0</td>\n",
       "      <td>166956.157270</td>\n",
       "      <td>73427.631167</td>\n",
       "      <td>10526.0</td>\n",
       "      <td>122764.00</td>\n",
       "      <td>153140.0</td>\n",
       "      <td>193660.00</td>\n",
       "      <td>588684.0</td>\n",
       "      <td>337.0</td>\n",
       "      <td>319.160237</td>\n",
       "      <td>501.174209</td>\n",
       "      <td>23.0</td>\n",
       "      <td>95.00</td>\n",
       "      <td>155.0</td>\n",
       "      <td>333.00</td>\n",
       "      <td>4504.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>134.0</td>\n",
       "      <td>158624.395522</td>\n",
       "      <td>58235.107164</td>\n",
       "      <td>54522.0</td>\n",
       "      <td>125180.75</td>\n",
       "      <td>149865.5</td>\n",
       "      <td>177997.75</td>\n",
       "      <td>519562.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>306.604478</td>\n",
       "      <td>529.415169</td>\n",
       "      <td>19.0</td>\n",
       "      <td>83.25</td>\n",
       "      <td>130.5</td>\n",
       "      <td>264.75</td>\n",
       "      <td>4627.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>789.0</td>\n",
       "      <td>163203.536122</td>\n",
       "      <td>55018.554941</td>\n",
       "      <td>61029.0</td>\n",
       "      <td>126180.00</td>\n",
       "      <td>153384.0</td>\n",
       "      <td>187954.00</td>\n",
       "      <td>499408.0</td>\n",
       "      <td>789.0</td>\n",
       "      <td>306.795944</td>\n",
       "      <td>512.004407</td>\n",
       "      <td>33.0</td>\n",
       "      <td>94.00</td>\n",
       "      <td>144.0</td>\n",
       "      <td>318.00</td>\n",
       "      <td>5239.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>50.0</td>\n",
       "      <td>174376.240000</td>\n",
       "      <td>98261.465056</td>\n",
       "      <td>74497.0</td>\n",
       "      <td>116150.50</td>\n",
       "      <td>155213.0</td>\n",
       "      <td>186984.75</td>\n",
       "      <td>551512.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>399.040000</td>\n",
       "      <td>803.445437</td>\n",
       "      <td>45.0</td>\n",
       "      <td>105.25</td>\n",
       "      <td>152.0</td>\n",
       "      <td>298.50</td>\n",
       "      <td>4576.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>176.0</td>\n",
       "      <td>171828.426136</td>\n",
       "      <td>80165.495562</td>\n",
       "      <td>66950.0</td>\n",
       "      <td>116440.00</td>\n",
       "      <td>156275.5</td>\n",
       "      <td>199908.25</td>\n",
       "      <td>557567.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>263.602273</td>\n",
       "      <td>373.997725</td>\n",
       "      <td>34.0</td>\n",
       "      <td>91.00</td>\n",
       "      <td>140.0</td>\n",
       "      <td>282.00</td>\n",
       "      <td>3341.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>224.0</td>\n",
       "      <td>150918.736607</td>\n",
       "      <td>47023.239428</td>\n",
       "      <td>56834.0</td>\n",
       "      <td>121091.00</td>\n",
       "      <td>143292.0</td>\n",
       "      <td>172203.00</td>\n",
       "      <td>360612.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>260.584821</td>\n",
       "      <td>353.481394</td>\n",
       "      <td>31.0</td>\n",
       "      <td>80.75</td>\n",
       "      <td>116.0</td>\n",
       "      <td>265.75</td>\n",
       "      <td>2570.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1638.0</td>\n",
       "      <td>156581.603175</td>\n",
       "      <td>55191.607456</td>\n",
       "      <td>0.0</td>\n",
       "      <td>117890.25</td>\n",
       "      <td>148663.5</td>\n",
       "      <td>184678.00</td>\n",
       "      <td>516636.0</td>\n",
       "      <td>1638.0</td>\n",
       "      <td>275.152625</td>\n",
       "      <td>400.663785</td>\n",
       "      <td>24.0</td>\n",
       "      <td>88.00</td>\n",
       "      <td>143.0</td>\n",
       "      <td>290.75</td>\n",
       "      <td>5358.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>268.0</td>\n",
       "      <td>157458.309701</td>\n",
       "      <td>54096.668084</td>\n",
       "      <td>57038.0</td>\n",
       "      <td>125849.50</td>\n",
       "      <td>149128.5</td>\n",
       "      <td>179946.50</td>\n",
       "      <td>452244.0</td>\n",
       "      <td>268.0</td>\n",
       "      <td>301.070896</td>\n",
       "      <td>486.320919</td>\n",
       "      <td>20.0</td>\n",
       "      <td>81.75</td>\n",
       "      <td>126.5</td>\n",
       "      <td>266.25</td>\n",
       "      <td>4022.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Revenue                                                   \\\n",
       "                     count           mean           std      min        25%   \n",
       "Education_Holiday                                                             \n",
       "0                  16354.0  164709.784395  61493.720897      0.0  124065.25   \n",
       "1                   2068.0  163259.782882  62768.253719      0.0  123148.50   \n",
       "2                    737.0  160878.067843  69131.438003  29402.0  115893.00   \n",
       "3                    565.0  132157.458407  51340.216441  50512.0   91984.00   \n",
       "4                   1348.0  112283.588279  49645.937981      0.0   77285.75   \n",
       "5                   3402.0  160819.537625  59511.431043      0.0  121279.50   \n",
       "6                    318.0  152861.103774  50373.018489      0.0  120447.00   \n",
       "7                    422.0  157708.246445  56935.759852  58486.0  125354.25   \n",
       "8                    351.0  150517.170940  45880.078449  72653.0  119001.50   \n",
       "9                    803.0  142876.703611  48064.566784  62801.0  109258.00   \n",
       "10                  4497.0  161025.534801  59507.827982      0.0  121298.00   \n",
       "11                   603.0  140894.966833  57462.132805   9334.0  102874.50   \n",
       "12                  1746.0  160743.247995  61455.488602  16672.0  121651.50   \n",
       "13                   337.0  166956.157270  73427.631167  10526.0  122764.00   \n",
       "14                   134.0  158624.395522  58235.107164  54522.0  125180.75   \n",
       "15                   789.0  163203.536122  55018.554941  61029.0  126180.00   \n",
       "17                    50.0  174376.240000  98261.465056  74497.0  116150.50   \n",
       "18                   176.0  171828.426136  80165.495562  66950.0  116440.00   \n",
       "19                   224.0  150918.736607  47023.239428  56834.0  121091.00   \n",
       "20                  1638.0  156581.603175  55191.607456      0.0  117890.25   \n",
       "28                   268.0  157458.309701  54096.668084  57038.0  125849.50   \n",
       "\n",
       "                                                 no_of_units              \\\n",
       "                        50%        75%       max       count        mean   \n",
       "Education_Holiday                                                          \n",
       "0                  154352.0  191331.00  659082.0     16354.0  300.587991   \n",
       "1                  153307.0  188643.75  619935.0      2068.0  291.819632   \n",
       "2                  149776.0  188163.00  646111.0       737.0  324.507463   \n",
       "3                  124905.0  161863.00  433570.0       565.0  260.702655   \n",
       "4                  101223.0  135326.00  427571.0      1348.0  208.701780   \n",
       "5                  151293.5  186777.00  650798.0      3402.0  278.445326   \n",
       "6                  150006.0  183002.50  358845.0       318.0  256.930818   \n",
       "7                  150233.5  177712.25  605400.0       422.0  313.784360   \n",
       "8                  141580.0  180527.00  402029.0       351.0  295.977208   \n",
       "9                  133907.0  166696.50  495786.0       803.0  311.260274   \n",
       "10                 151713.0  186302.00  588033.0      4497.0  289.724705   \n",
       "11                 130289.0  169307.50  497997.0       603.0  275.900498   \n",
       "12                 152021.0  185542.75  607398.0      1746.0  285.931844   \n",
       "13                 153140.0  193660.00  588684.0       337.0  319.160237   \n",
       "14                 149865.5  177997.75  519562.0       134.0  306.604478   \n",
       "15                 153384.0  187954.00  499408.0       789.0  306.795944   \n",
       "17                 155213.0  186984.75  551512.0        50.0  399.040000   \n",
       "18                 156275.5  199908.25  557567.0       176.0  263.602273   \n",
       "19                 143292.0  172203.00  360612.0       224.0  260.584821   \n",
       "20                 148663.5  184678.00  516636.0      1638.0  275.152625   \n",
       "28                 149128.5  179946.50  452244.0       268.0  301.070896   \n",
       "\n",
       "                                                                    \n",
       "                          std   min     25%    50%     75%     max  \n",
       "Education_Holiday                                                   \n",
       "0                  456.582979  12.0   91.00  150.0  312.75  7296.0  \n",
       "1                  450.671307  29.0   93.00  143.5  297.25  4846.0  \n",
       "2                  558.060581  22.0   90.00  153.0  296.00  4612.0  \n",
       "3                  437.836335  22.0   69.00  124.0  267.00  4396.0  \n",
       "4                  329.739329   8.0   60.00  101.5  209.50  3295.0  \n",
       "5                  425.227353  25.0   87.00  142.5  285.75  5210.0  \n",
       "6                  490.349050  35.0   86.00  127.0  258.75  5329.0  \n",
       "7                  535.078020  35.0   86.25  145.5  304.00  5136.0  \n",
       "8                  443.389928  32.0   85.00  139.0  313.50  3534.0  \n",
       "9                  509.165888  26.0   80.50  145.0  298.00  4413.0  \n",
       "10                 438.079835  18.0   90.00  147.0  299.00  5220.0  \n",
       "11                 409.994194  11.0   77.00  136.0  298.00  3379.0  \n",
       "12                 427.782160  19.0   89.00  143.5  290.00  4704.0  \n",
       "13                 501.174209  23.0   95.00  155.0  333.00  4504.0  \n",
       "14                 529.415169  19.0   83.25  130.5  264.75  4627.0  \n",
       "15                 512.004407  33.0   94.00  144.0  318.00  5239.0  \n",
       "17                 803.445437  45.0  105.25  152.0  298.50  4576.0  \n",
       "18                 373.997725  34.0   91.00  140.0  282.00  3341.0  \n",
       "19                 353.481394  31.0   80.75  116.0  265.75  2570.0  \n",
       "20                 400.663785  24.0   88.00  143.0  290.75  5358.0  \n",
       "28                 486.320919  20.0   81.75  126.5  266.25  4022.0  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Education holiday variable in monthly dataset\n",
    "monthly[['Education_Holiday','Revenue','no_of_units']].groupby('Education_Holiday').describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "gpdhiIWTugkf"
   },
   "outputs": [],
   "source": [
    "#removing unwanted columns in the dataset\n",
    "weekly.drop(['year','Revenue','Education_Holiday'],axis=1,inplace=True)\n",
    "monthly.drop(['year','Revenue','Education_Holiday'],axis=1,inplace=True)\n",
    "quarter.drop(['year','Revenue','Education_Holiday'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DEej1NdhvjGs"
   },
   "source": [
    "## Building Model for Weekly dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(weekly.corr(),annot=True,cmap='RdBu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████| 146025/146025 [02:03<00:00, 1181.90it/s]\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "for i in tqdm(range(0,len(weekly))):\n",
    "    if(weekly.Promotion_applied[i]>0):\n",
    "        weekly.Promotion_applied[i] = 1\n",
    "    else:\n",
    "        weekly.Promotion_applied[i] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">no_of_units</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Promotion_applied</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>68265.0</td>\n",
       "      <td>61.350297</td>\n",
       "      <td>97.302058</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>1520.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>77760.0</td>\n",
       "      <td>83.970383</td>\n",
       "      <td>127.701186</td>\n",
       "      <td>1.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>2423.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  no_of_units                                                \\\n",
       "                        count       mean         std  min   25%   50%   75%   \n",
       "Promotion_applied                                                             \n",
       "0                     68265.0  61.350297   97.302058  1.0  18.0  30.0  63.0   \n",
       "1                     77760.0  83.970383  127.701186  1.0  26.0  42.0  87.0   \n",
       "\n",
       "                           \n",
       "                      max  \n",
       "Promotion_applied          \n",
       "0                  1520.0  \n",
       "1                  2423.0  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#promotion applied variable in monthly dataset\n",
    "weekly[['Promotion_applied','no_of_units']].groupby('Promotion_applied').describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "Ywh78rcrvLIJ"
   },
   "outputs": [],
   "source": [
    "#consider weekly dataset\n",
    "x = weekly.drop('no_of_units',axis=1)\n",
    "y = weekly.no_of_units"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "-VLl7HQOvLzu"
   },
   "outputs": [],
   "source": [
    "#transforming the output variable\n",
    "y1 = np.round(np.log(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "id": "7rtBie51WYGP",
    "outputId": "2b3fa3cd-62f1-4b37-b0a9-94f7310c5a36"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Product_type</th>\n",
       "      <th>Promotion_applied</th>\n",
       "      <th>Generic_Holiday</th>\n",
       "      <th>updated_week</th>\n",
       "      <th>updated_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000898</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.001795</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.002693</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.003591</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>146020</th>\n",
       "      <td>0.996409</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.818182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146021</th>\n",
       "      <td>0.997307</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.818182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146022</th>\n",
       "      <td>0.998205</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.818182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146023</th>\n",
       "      <td>0.999102</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.818182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146024</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.818182</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>146025 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Product_type  Promotion_applied  Generic_Holiday  updated_week  \\\n",
       "0           0.000000                1.0              0.0      0.000000   \n",
       "1           0.000898                1.0              0.0      0.000000   \n",
       "2           0.001795                1.0              0.0      0.000000   \n",
       "3           0.002693                1.0              0.0      0.000000   \n",
       "4           0.003591                1.0              0.0      0.000000   \n",
       "...              ...                ...              ...           ...   \n",
       "146020      0.996409                0.0              0.5      0.666667   \n",
       "146021      0.997307                0.0              0.5      0.666667   \n",
       "146022      0.998205                0.0              0.5      0.666667   \n",
       "146023      0.999102                0.0              0.5      0.666667   \n",
       "146024      1.000000                0.0              0.5      0.666667   \n",
       "\n",
       "        updated_month  \n",
       "0            0.000000  \n",
       "1            0.000000  \n",
       "2            0.000000  \n",
       "3            0.000000  \n",
       "4            0.000000  \n",
       "...               ...  \n",
       "146020       0.818182  \n",
       "146021       0.818182  \n",
       "146022       0.818182  \n",
       "146023       0.818182  \n",
       "146024       0.818182  \n",
       "\n",
       "[146025 rows x 5 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#preprocessing the data i.e., applying min-max scalar\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "#appling scaling for weekly data\n",
    "scale_week = MinMaxScaler()\n",
    "a = scale_week.fit_transform(x)\n",
    "x1 = pd.DataFrame(a,columns=x.columns)\n",
    "x1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#exporting the pickle files\n",
    "import pickle\n",
    "file_out = open('scale_week.pkl','wb')\n",
    "pickle.dump(scale_week,file_out)\n",
    "file_out.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_R__GemzwtBy",
    "outputId": "5e6874d8-7789-4ed7-ed09-24a660817a1a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of Splitting\n",
      "x_train=(116820, 5), y_train=(116820,), x_test=(29205, 5),y_test=(29205,)\n"
     ]
    }
   ],
   "source": [
    "#Splitting into train and test\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(x1,y1,test_size=0.2,random_state=2)\n",
    "\n",
    "print('Shape of Splitting')\n",
    "print('x_train={}, y_train={}, x_test={},y_test={}'.format(x_train.shape,y_train.shape,x_test.shape,y_test.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "id": "QfznIA6uWiLG"
   },
   "outputs": [],
   "source": [
    "#Building the linear Regression Model\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "#initializing the model\n",
    "lin = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "riLehMZMv6Yb",
    "outputId": "e460c0ef-3328-46b8-924b-9e55e1f1ebcd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9878981854314998\n",
      "r2 score :  0.0335698055003848\n"
     ]
    }
   ],
   "source": [
    "#fitting and predicting for test data\n",
    "pred_lin = lin.fit(x_train,y_train).predict(x_test)\n",
    "\n",
    "#calculating RMSE value\n",
    "RMSE_lin = np.sqrt(mean_squared_error(y_test,pred_lin))\n",
    "print(RMSE_lin)\n",
    "\n",
    "#calculating r2 score\n",
    "r2_lin = r2_score(y_test,pred_lin)\n",
    "print('r2 score : ',r2_lin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "id": "zRvyB594xyfi"
   },
   "outputs": [],
   "source": [
    "#building Random Forest Model\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "#initializing the model\n",
    "rand = RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "maCORoIRybce",
    "outputId": "4b743234-44cb-4e53-abf8-a454c78882f0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE value :  0.30260877487481685\n",
      "r2 score :  0.9093204931846973\n"
     ]
    }
   ],
   "source": [
    "#fitting and predicting for test data\n",
    "pred_rand = rand.fit(x_train,y_train).predict(x_test)\n",
    "\n",
    "#calculating RMSE value\n",
    "RMSE_rand = np.sqrt(mean_squared_error(y_test,pred_rand))\n",
    "print('RMSE value : ',RMSE_rand)\n",
    "\n",
    "#calculating r2 score\n",
    "r2_rand = r2_score(y_test,pred_rand)\n",
    "print('r2 score : ',r2_rand)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "file_out = open('rand_week.pkl','wb')\n",
    "pickle.dump(rand,file_out)\n",
    "file_out.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "id": "4ch25bJaykQi"
   },
   "outputs": [],
   "source": [
    "#hyper parameter tuning\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "p_HAWYeXyJ7L",
    "outputId": "050dcbf3-c1bf-45de-9e13-76cd4e3b721c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['bootstrap', 'ccp_alpha', 'criterion', 'max_depth', 'max_features', 'max_leaf_nodes', 'max_samples', 'min_impurity_decrease', 'min_impurity_split', 'min_samples_leaf', 'min_samples_split', 'min_weight_fraction_leaf', 'n_estimators', 'n_jobs', 'oob_score', 'random_state', 'verbose', 'warm_start'])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rand.get_params().keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "j9EFiXeHwPme",
    "outputId": "acdd8363-9c62-4e48-da39-4dd4b489e52e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 27 candidates, totalling 81 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  4.9min\n",
      "[Parallel(n_jobs=-1)]: Done  81 out of  81 | elapsed: 12.6min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE value :  0.2741817047854829\n",
      "r2 score :  0.9255571383065931\n"
     ]
    }
   ],
   "source": [
    "#hyper-parameters\n",
    "params = {'n_estimators': [50,100,150],\n",
    "          'max_depth': [30,40,50],\n",
    "          #'min_samples_split' : [2, 5, 10, 15],\n",
    "          'min_samples_leaf' : [10,15,20]\n",
    "}\n",
    "\n",
    "#initializing grid\n",
    "grid_rand = GridSearchCV(rand,params,verbose=2,n_jobs=-1,cv=3)\n",
    "\n",
    "#fitting to train data\n",
    "grid_rand.fit(x_train,y_train)\n",
    "\n",
    "#fitting and predicting for test data\n",
    "pred_rand = grid_rand.predict(x_test)\n",
    "\n",
    "#calculating RMSE value\n",
    "RMSE_rand = np.sqrt(mean_squared_error(y_test,pred_rand))\n",
    "print('RMSE value : ',RMSE_rand)\n",
    "\n",
    "#calculating r2 score\n",
    "r2_rand = r2_score(y_test,pred_rand)\n",
    "print('r2 score : ',r2_rand)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "id": "0qwpwhjmz8aU"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Score : 0.9234672372348575\n",
      "Best Params : {'max_depth': 50, 'min_samples_leaf': 10, 'n_estimators': 100}\n"
     ]
    }
   ],
   "source": [
    "print('Best Score :',grid_rand.best_score_)\n",
    "print('Best Params :',grid_rand.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "id": "EpH4M4aVeY3n"
   },
   "outputs": [],
   "source": [
    "#Building the XGBoost model\n",
    "from xgboost import XGBRegressor\n",
    "\n",
    "#initializing the model\n",
    "xgb = XGBRegressor(n_estimators=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "id": "FK2uti7Ji_r2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE value :  0.2690991986252341\n",
      "r2 score :  0.9282914528233901\n"
     ]
    }
   ],
   "source": [
    "pred_xgb = xgb.fit(x_train,y_train).predict(x_test)\n",
    "\n",
    "#calculating RMSE value\n",
    "RMSE_xgb = np.sqrt(mean_squared_error(y_test,pred_xgb))\n",
    "print('RMSE value : ',RMSE_xgb)\n",
    "\n",
    "#calculating r2 score\n",
    "r2_xgb = r2_score(y_test,pred_xgb)\n",
    "print('r2 score : ',r2_xgb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "file_out = open('week.pkl','wb')\n",
    "pickle.dump(xgb,file_out)\n",
    "file_out.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['objective', 'base_score', 'booster', 'colsample_bylevel', 'colsample_bynode', 'colsample_bytree', 'gamma', 'gpu_id', 'importance_type', 'interaction_constraints', 'learning_rate', 'max_delta_step', 'max_depth', 'min_child_weight', 'missing', 'monotone_constraints', 'n_estimators', 'n_jobs', 'num_parallel_tree', 'random_state', 'reg_alpha', 'reg_lambda', 'scale_pos_weight', 'subsample', 'tree_method', 'validate_parameters', 'verbosity'])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hyper-parameters\n",
    "xgb.get_params().keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "iaw1k2F3jTo3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 12 candidates, totalling 36 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    }
   ],
   "source": [
    "#hyper-parameters\n",
    "params = {'learning_rate': [0.01, 0.1],\n",
    "        'max_depth': [20,30,40],\n",
    "        'n_estimators' : [100,200]\n",
    "    }\n",
    "\n",
    "#initializing grid\n",
    "grid_xgb = GridSearchCV(xgb,params,verbose=2,n_jobs=-1,cv=3)\n",
    "\n",
    "#fitting to train data\n",
    "grid_xgb.fit(x_train,y_train)\n",
    "\n",
    "#fitting and predicting for test data\n",
    "pred_xgb = grid_xgb.predict(x_test)\n",
    "\n",
    "#calculating RMSE value\n",
    "RMSE_xgb = np.sqrt(mean_squared_error(y_test,pred_xgb))\n",
    "print('RMSE value : ',RMSE_xgb)\n",
    "\n",
    "#calculating r2 score\n",
    "r2_xgb = r2_score(y_test,pred_xgb)\n",
    "print('r2 score : ',r2_xgb)\n",
    "\n",
    "print('Best Score :',grid_xgb.best_score_)\n",
    "print('Best Params :',grid_xgb.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Building Decision Tree Model\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "#initializing the model\n",
    "deci = DecisionTreeRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE value :  0.3583530908740311\n",
      "r2 score :  0.8728347573596416\n"
     ]
    }
   ],
   "source": [
    "#fitting and predicting for test data\n",
    "pred_deci = deci.fit(x_train,y_train).predict(x_test)\n",
    "\n",
    "#calculating RMSE value\n",
    "RMSE_deci = np.sqrt(mean_squared_error(y_test,pred_deci))\n",
    "print('RMSE value : ',RMSE_deci)\n",
    "\n",
    "#calculating r2 score\n",
    "r2_deci = r2_score(y_test,pred_deci)\n",
    "print('r2 score : ',r2_deci)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['ccp_alpha', 'criterion', 'max_depth', 'max_features', 'max_leaf_nodes', 'min_impurity_decrease', 'min_impurity_split', 'min_samples_leaf', 'min_samples_split', 'min_weight_fraction_leaf', 'presort', 'random_state', 'splitter'])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hyper parameters\n",
    "deci.get_params().keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#hyper-parameters\n",
    "params = {'min_samples_split': [20,30,50],\n",
    "          'max_depth': [50,100,150],\n",
    "          'min_samples_leaf': [10,20,30]\n",
    "    }\n",
    "\n",
    "#initializing grid\n",
    "grid_deci = GridSearchCV(deci,params,verbose=2,n_jobs=-1,cv=3)\n",
    "\n",
    "#fitting to train data\n",
    "grid_deci.fit(x_train,y_train)\n",
    "\n",
    "#fitting and predicting for test data\n",
    "pred_deci = grid_deci.predict(x_test)\n",
    "\n",
    "#calculating RMSE value\n",
    "RMSE_deci = np.sqrt(mean_squared_error(y_test,pred_deci))\n",
    "print('RMSE value : ',RMSE_deci)\n",
    "\n",
    "#calculating r2 score\n",
    "r2_deci = r2_score(y_test,pred_deci)\n",
    "print('r2 score : ',r2_deci)\n",
    "\n",
    "print('Best Score :',grid_deci.best_score_)\n",
    "print('Best Params :',grid_deci.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Building KNN model\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "\n",
    "#initializing the model\n",
    "knn = KNeighborsRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE value :  1.0085774916951005\n",
      "r2 score :  -0.007313509556341025\n"
     ]
    }
   ],
   "source": [
    "#fitting and predicting for test data\n",
    "pred_knn = knn.fit(x_train,y_train).predict(x_test)\n",
    "\n",
    "#calculating RMSE value\n",
    "RMSE_knn = np.sqrt(mean_squared_error(y_test,pred_knn))\n",
    "print('RMSE value : ',RMSE_knn)\n",
    "\n",
    "#calculating r2 score\n",
    "r2_knn = r2_score(y_test,pred_knn)\n",
    "print('r2 score : ',r2_knn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['algorithm', 'leaf_size', 'metric', 'metric_params', 'n_jobs', 'n_neighbors', 'p', 'weights'])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hyper parameters\n",
    "knn.get_params().keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#hyper-parameters\n",
    "params = {'n_neighbors': range(30,50,2)\n",
    "    }\n",
    "\n",
    "#initializing grid\n",
    "grid_knn = GridSearchCV(knn,params,verbose=2,n_jobs=-1,cv=3)\n",
    "\n",
    "#fitting to train data\n",
    "grid_knn.fit(x_train,y_train)\n",
    "\n",
    "#fitting and predicting for test data\n",
    "pred_knn = grid_knn.predict(x_test)\n",
    "\n",
    "#calculating RMSE value\n",
    "RMSE_knn = np.sqrt(mean_squared_error(y_test,pred_knn))\n",
    "print('RMSE value : ',RMSE_knn)\n",
    "\n",
    "#calculating r2 score\n",
    "r2_knn = r2_score(y_test,pred_knn)\n",
    "print('r2 score : ',r2_knn)\n",
    "\n",
    "print('Best Score :',grid_knn.best_score_)\n",
    "print('Best Params :',grid_knn.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RMSE</th>\n",
       "      <th>r2_Score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algorithm</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Linear Reg</th>\n",
       "      <td>0.987898</td>\n",
       "      <td>0.033570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNN</th>\n",
       "      <td>1.008577</td>\n",
       "      <td>-0.007314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.358353</td>\n",
       "      <td>0.872835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>0.274182</td>\n",
       "      <td>0.925557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XGBoost</th>\n",
       "      <td>0.512601</td>\n",
       "      <td>0.739802</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   RMSE  r2_Score\n",
       "Algorithm                        \n",
       "Linear Reg     0.987898  0.033570\n",
       "KNN            1.008577 -0.007314\n",
       "Decision Tree  0.358353  0.872835\n",
       "Random Forest  0.274182  0.925557\n",
       "XGBoost        0.512601  0.739802"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#consolidating the results\n",
    "Algorithm = ['Linear Reg','KNN','Decision Tree','Random Forest','XGBoost']\n",
    "RMSE = [RMSE_lin,RMSE_knn,RMSE_deci,RMSE_rand,RMSE_xgb]\n",
    "r2_score = [r2_lin,r2_knn,r2_deci,r2_rand,r2_xgb]\n",
    "weekly_result = pd.DataFrame({'Algorithm':Algorithm,'RMSE':RMSE,'r2_Score':r2_score})\n",
    "weekly_result = weekly_result.set_index('Algorithm')\n",
    "weekly_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "#exporting the pickle file\n",
    "import pickle\n",
    "pickle_out = open('pickle_files/weekly.pkl','wb')\n",
    "pickle.dump(grid_rand,pickle_out)\n",
    "pickle_out.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([57.23208154])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Loading Pickle file\n",
    "model = pickle.load(open('pickle_files/weekly.pkl','rb'))\n",
    "\n",
    "#Scaling the input data\n",
    "i = [1,1,0,1,1]\n",
    "m = scale_week.transform([i])\n",
    "m = pd.DataFrame(m,columns=weekly.columns[:-1])\n",
    "\n",
    "#predicting the output\n",
    "np.exp(model.predict(m))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "weekly"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building model for Monthly dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(monthly.corr(),annot=True,cmap='RdBu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">no_of_units</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Promotion_applied</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2230.0</td>\n",
       "      <td>217.498655</td>\n",
       "      <td>347.248416</td>\n",
       "      <td>8.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>221.00</td>\n",
       "      <td>4760.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>180.0</td>\n",
       "      <td>236.700000</td>\n",
       "      <td>368.102198</td>\n",
       "      <td>26.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>227.25</td>\n",
       "      <td>2724.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>28090.0</td>\n",
       "      <td>292.110253</td>\n",
       "      <td>449.324532</td>\n",
       "      <td>12.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>300.00</td>\n",
       "      <td>7296.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>6330.0</td>\n",
       "      <td>313.524487</td>\n",
       "      <td>480.021407</td>\n",
       "      <td>18.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>323.00</td>\n",
       "      <td>7166.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  no_of_units                                             \\\n",
       "                        count        mean         std   min   25%    50%   \n",
       "Promotion_applied                                                          \n",
       "5                      2230.0  217.498655  347.248416   8.0  64.0  108.0   \n",
       "6                       180.0  236.700000  368.102198  26.0  65.0  116.0   \n",
       "10                    28090.0  292.110253  449.324532  12.0  89.0  145.0   \n",
       "15                     6330.0  313.524487  480.021407  18.0  96.0  157.0   \n",
       "\n",
       "                                   \n",
       "                      75%     max  \n",
       "Promotion_applied                  \n",
       "5                  221.00  4760.0  \n",
       "6                  227.25  2724.0  \n",
       "10                 300.00  7296.0  \n",
       "15                 323.00  7166.0  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#promotion applied variable in monthly dataset\n",
    "monthly[['Promotion_applied','no_of_units']].groupby('Promotion_applied').describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████| 36830/36830 [00:24<00:00, 1494.14it/s]\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "monthly['Promotion'] = 0\n",
    "for i in tqdm(range(0,len(monthly))):\n",
    "    if(monthly.Promotion_applied[i]<6):\n",
    "        monthly['Promotion'][i] = 0\n",
    "    else:\n",
    "        monthly['Promotion'][i] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">no_of_units</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Promotion</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2230.0</td>\n",
       "      <td>217.498655</td>\n",
       "      <td>347.248416</td>\n",
       "      <td>8.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>221.0</td>\n",
       "      <td>4760.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34600.0</td>\n",
       "      <td>295.739682</td>\n",
       "      <td>454.799451</td>\n",
       "      <td>12.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>304.0</td>\n",
       "      <td>7296.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          no_of_units                                                    \\\n",
       "                count        mean         std   min   25%    50%    75%   \n",
       "Promotion                                                                 \n",
       "0              2230.0  217.498655  347.248416   8.0  64.0  108.0  221.0   \n",
       "1             34600.0  295.739682  454.799451  12.0  90.0  147.0  304.0   \n",
       "\n",
       "                   \n",
       "              max  \n",
       "Promotion          \n",
       "0          4760.0  \n",
       "1          7296.0  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#promotion applied variable in monthly dataset\n",
    "monthly[['Promotion','no_of_units']].groupby('Promotion').describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>updated_month</th>\n",
       "      <th>Product_type</th>\n",
       "      <th>Generic_Holiday</th>\n",
       "      <th>Promotion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000898</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001795</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002693</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003591</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36825</th>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.996409</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36826</th>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.997307</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36827</th>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.998205</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36828</th>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.999102</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36829</th>\n",
       "      <td>0.818182</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>36830 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       updated_month  Product_type  Generic_Holiday  Promotion\n",
       "0           0.000000      0.000000             0.00        1.0\n",
       "1           0.000000      0.000898             0.00        1.0\n",
       "2           0.000000      0.001795             0.00        1.0\n",
       "3           0.000000      0.002693             0.00        1.0\n",
       "4           0.000000      0.003591             0.00        1.0\n",
       "...              ...           ...              ...        ...\n",
       "36825       0.818182      0.996409             0.25        0.0\n",
       "36826       0.818182      0.997307             0.25        0.0\n",
       "36827       0.818182      0.998205             0.25        0.0\n",
       "36828       0.818182      0.999102             0.25        0.0\n",
       "36829       0.818182      1.000000             0.25        0.0\n",
       "\n",
       "[36830 rows x 4 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating input and output\n",
    "x = monthly.drop(['no_of_units','Promotion_applied'],axis=1)\n",
    "y = monthly.no_of_units\n",
    "\n",
    "#transforming the output variable\n",
    "y1 = np.round(np.log(y))\n",
    "\n",
    "#appling scaling for weekly data\n",
    "scale_month = MinMaxScaler()\n",
    "a = scale_month.fit_transform(x)\n",
    "x1 = pd.DataFrame(a,columns=x.columns)\n",
    "x1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "#exporting the pickle files\n",
    "import pickle\n",
    "file_out = open('scale_month.pkl','wb')\n",
    "pickle.dump(scale_month,file_out)\n",
    "file_out.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of Splitting\n",
      "x_train=(29464, 4), y_train=(29464,), x_test=(7366, 4),y_test=(7366,)\n"
     ]
    }
   ],
   "source": [
    "#Splitting into train and test\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(x1,y1,test_size=0.2,random_state=2)\n",
    "\n",
    "print('Shape of Splitting')\n",
    "print('x_train={}, y_train={}, x_test={},y_test={}'.format(x_train.shape,y_train.shape,x_test.shape,y_test.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9697797320469475\n",
      "r2 score :  0.009871861456406283\n"
     ]
    }
   ],
   "source": [
    "#Building the linear Regression Model\n",
    "from sklearn.metrics import r2_score\n",
    "#initializing the model\n",
    "lin = LinearRegression()\n",
    "\n",
    "#fitting and predicting for test data\n",
    "pred_lin = lin.fit(x_train,y_train).predict(x_test)\n",
    "\n",
    "#calculating RMSE value\n",
    "RMSE_lin = np.sqrt(mean_squared_error(y_test,pred_lin))\n",
    "print(RMSE_lin)\n",
    "\n",
    "#calculating r2 score\n",
    "r2_lin = r2_score(y_test,pred_lin)\n",
    "print('r2 score : ',r2_lin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE value :  0.3812908116356503\n",
      "r2 score :  0.8469413509381438\n"
     ]
    }
   ],
   "source": [
    "#building Random Forest Model\n",
    "from sklearn.metrics import r2_score\n",
    "#initializing the model\n",
    "rand = RandomForestRegressor()\n",
    "\n",
    "#fitting and predicting for test data\n",
    "pred_rand = rand.fit(x_train,y_train).predict(x_test)\n",
    "\n",
    "#calculating RMSE value\n",
    "RMSE_rand = np.sqrt(mean_squared_error(y_test,pred_rand))\n",
    "print('RMSE value : ',RMSE_rand)\n",
    "\n",
    "#calculating r2 score\n",
    "r2_rand = r2_score(y_test,pred_rand)\n",
    "print('r2 score : ',r2_rand)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 81 candidates, totalling 243 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  2.1min\n",
      "[Parallel(n_jobs=-1)]: Done 154 tasks      | elapsed:  9.5min\n",
      "[Parallel(n_jobs=-1)]: Done 243 out of 243 | elapsed: 14.8min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE value :  0.34013894560401675\n",
      "r2 score :  0.8781970186651212\n",
      "Best Score : 0.8727268050543028\n",
      "Best Params : {'max_depth': 70, 'min_samples_leaf': 5, 'min_samples_split': 2, 'n_estimators': 200}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "#hyper-parameters\n",
    "params = {'n_estimators': [150,200,250],\n",
    "          'max_depth': [50,60,70],\n",
    "          'min_samples_split' : [2, 5, 10],\n",
    "          'min_samples_leaf' : [3,5,10]\n",
    "}\n",
    "\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "#initializing grid\n",
    "grid_rand = GridSearchCV(rand,params,verbose=2,n_jobs=-1,cv=3)\n",
    "\n",
    "#fitting to train data\n",
    "grid_rand.fit(x_train,y_train)\n",
    "\n",
    "#fitting and predicting for test data\n",
    "pred_rand = grid_rand.predict(x_test)\n",
    "\n",
    "#calculating RMSE value\n",
    "RMSE_rand = np.sqrt(mean_squared_error(y_test,pred_rand))\n",
    "print('RMSE value : ',RMSE_rand)\n",
    "\n",
    "#calculating r2 score\n",
    "r2_rand = r2_score(y_test,pred_rand)\n",
    "print('r2 score : ',r2_rand)\n",
    "\n",
    "print('Best Score :',grid_rand.best_score_)\n",
    "print('Best Params :',grid_rand.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE value :  0.2910755269583804\n",
      "r2 score :  0.9108016992471651\n"
     ]
    }
   ],
   "source": [
    "#Building the XGBoost model\n",
    "from xgboost import XGBRegressor\n",
    "\n",
    "#initializing the model\n",
    "xgb = XGBRegressor(learning_rate=0.1, max_depth=10, min_child_weight=5, n_estimators=450)\n",
    "\n",
    "#fitting and predicting for test data\n",
    "pred_xgb = xgb.fit(x_train,y_train).predict(x_test)\n",
    "\n",
    "#calculating RMSE value\n",
    "RMSE_xgb = np.sqrt(mean_squared_error(y_test,pred_xgb))\n",
    "print('RMSE value : ',RMSE_xgb)\n",
    "\n",
    "#calculating r2 score\n",
    "r2_xgb = r2_score(y_test,pred_xgb)\n",
    "print('r2 score : ',r2_xgb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle_out = open('month.pkl','wb')\n",
    "pickle.dump(xgb,pickle_out)\n",
    "pickle_out.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 54 candidates, totalling 162 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  2.2min\n",
      "[Parallel(n_jobs=-1)]: Done 154 tasks      | elapsed: 10.7min\n",
      "[Parallel(n_jobs=-1)]: Done 162 out of 162 | elapsed: 11.4min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE value :  0.2910755269583804\n",
      "r2 score :  0.9108016992471651\n",
      "Best Score : 0.9008352133096382\n",
      "Best Params : {'learning_rate': 0.1, 'max_depth': 10, 'min_child_weight': 5, 'n_estimators': 450}\n"
     ]
    }
   ],
   "source": [
    "#hyper-parameters\n",
    "params = {'learning_rate': [0.01, 0.1],\n",
    "          'max_depth': [5,10,15],\n",
    "          'n_estimators' : [450,500,600],\n",
    "          'min_child_weight': [5,6,7]\n",
    "    }\n",
    "\n",
    "#initializing grid\n",
    "grid_xgb = GridSearchCV(xgb,params,verbose=2,n_jobs=-1,cv=3)\n",
    "\n",
    "#fitting to train data\n",
    "grid_xgb.fit(x_train,y_train)\n",
    "\n",
    "#fitting and predicting for test data\n",
    "pred_xgb = grid_xgb.predict(x_test)\n",
    "\n",
    "#calculating RMSE value\n",
    "RMSE_xgb = np.sqrt(mean_squared_error(y_test,pred_xgb))\n",
    "print('RMSE value : ',RMSE_xgb)\n",
    "\n",
    "#calculating r2 score\n",
    "r2_xgb = r2_score(y_test,pred_xgb)\n",
    "print('r2 score : ',r2_xgb)\n",
    "\n",
    "print('Best Score :',grid_xgb.best_score_)\n",
    "print('Best Params :',grid_xgb.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE value :  0.46087536286891767\n",
      "r2 score :  0.7763792079329785\n"
     ]
    }
   ],
   "source": [
    "#Building Decision Tree Model\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "#initializing the model\n",
    "deci = DecisionTreeRegressor()\n",
    "\n",
    "#fitting and predicting for test data\n",
    "pred_deci = deci.fit(x_train,y_train).predict(x_test)\n",
    "\n",
    "#calculating RMSE value\n",
    "RMSE_deci = np.sqrt(mean_squared_error(y_test,pred_deci))\n",
    "print('RMSE value : ',RMSE_deci)\n",
    "\n",
    "#calculating r2 score\n",
    "r2_deci = r2_score(y_test,pred_deci)\n",
    "print('r2 score : ',r2_deci)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 27 candidates, totalling 81 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:    4.9s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE value :  0.3518222100377702\n",
      "r2 score :  0.869685812351259\n",
      "Best Score : 0.8565826833589497\n",
      "Best Params : {'max_depth': 150, 'min_samples_leaf': 10, 'min_samples_split': 20}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  81 out of  81 | elapsed:    7.9s finished\n"
     ]
    }
   ],
   "source": [
    "#hyper-parameters\n",
    "params = {'min_samples_split': [20,30,50],\n",
    "          'max_depth': [50,100,150],\n",
    "          'min_samples_leaf': [10,20,30]\n",
    "    }\n",
    "\n",
    "#initializing grid\n",
    "grid_deci = GridSearchCV(deci,params,verbose=2,n_jobs=-1,cv=3)\n",
    "\n",
    "#fitting to train data\n",
    "grid_deci.fit(x_train,y_train)\n",
    "\n",
    "#fitting and predicting for test data\n",
    "pred_deci = grid_deci.predict(x_test)\n",
    "\n",
    "#calculating RMSE value\n",
    "RMSE_deci = np.sqrt(mean_squared_error(y_test,pred_deci))\n",
    "print('RMSE value : ',RMSE_deci)\n",
    "\n",
    "#calculating r2 score\n",
    "r2_deci = r2_score(y_test,pred_deci)\n",
    "print('r2 score : ',r2_deci)\n",
    "\n",
    "print('Best Score :',grid_deci.best_score_)\n",
    "print('Best Params :',grid_deci.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE value :  0.9860030472729812\n",
      "r2 score :  -0.023532672798106002\n"
     ]
    }
   ],
   "source": [
    "#Building KNN model\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "\n",
    "#initializing the model\n",
    "knn = KNeighborsRegressor()\n",
    "\n",
    "#fitting and predicting for test data\n",
    "pred_knn = knn.fit(x_train,y_train).predict(x_test)\n",
    "\n",
    "#calculating RMSE value\n",
    "RMSE_knn = np.sqrt(mean_squared_error(y_test,pred_knn))\n",
    "print('RMSE value : ',RMSE_knn)\n",
    "\n",
    "#calculating r2 score\n",
    "r2_knn = r2_score(y_test,pred_knn)\n",
    "print('r2 score : ',r2_knn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  30 out of  30 | elapsed:   10.9s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE value :  0.9732585250908365\n",
      "r2 score :  0.002755546828339561\n",
      "Best Score : 0.00024093778836705346\n",
      "Best Params : {'n_neighbors': 48}\n"
     ]
    }
   ],
   "source": [
    "#hyper-parameters\n",
    "params = {'n_neighbors': range(30,50,2)\n",
    "    }\n",
    "\n",
    "#initializing grid\n",
    "grid_knn = GridSearchCV(knn,params,verbose=2,n_jobs=-1,cv=3)\n",
    "\n",
    "#fitting to train data\n",
    "grid_knn.fit(x_train,y_train)\n",
    "\n",
    "#fitting and predicting for test data\n",
    "pred_knn = grid_knn.predict(x_test)\n",
    "\n",
    "#calculating RMSE value\n",
    "RMSE_knn = np.sqrt(mean_squared_error(y_test,pred_knn))\n",
    "print('RMSE value : ',RMSE_knn)\n",
    "\n",
    "#calculating r2 score\n",
    "r2_knn = r2_score(y_test,pred_knn)\n",
    "print('r2 score : ',r2_knn)\n",
    "\n",
    "print('Best Score :',grid_knn.best_score_)\n",
    "print('Best Params :',grid_knn.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RMSE</th>\n",
       "      <th>r2_Score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algorithm</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Linear Reg</th>\n",
       "      <td>0.969780</td>\n",
       "      <td>0.009872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNN</th>\n",
       "      <td>0.986003</td>\n",
       "      <td>-0.023533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.460875</td>\n",
       "      <td>0.776379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>0.340139</td>\n",
       "      <td>0.878197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XGBoost</th>\n",
       "      <td>0.291076</td>\n",
       "      <td>0.910802</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   RMSE  r2_Score\n",
       "Algorithm                        \n",
       "Linear Reg     0.969780  0.009872\n",
       "KNN            0.986003 -0.023533\n",
       "Decision Tree  0.460875  0.776379\n",
       "Random Forest  0.340139  0.878197\n",
       "XGBoost        0.291076  0.910802"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#consolidating the results\n",
    "Algorithm = ['Linear Reg','KNN','Decision Tree','Random Forest','XGBoost']\n",
    "RMSE = [RMSE_lin,RMSE_knn,RMSE_deci,RMSE_rand,RMSE_xgb]\n",
    "r2_score = [r2_lin,r2_knn,r2_deci,r2_rand,r2_xgb]\n",
    "monthly_result = pd.DataFrame({'Algorithm':Algorithm,'RMSE':RMSE,'r2_Score':r2_score})\n",
    "monthly_result = monthly_result.set_index('Algorithm')\n",
    "monthly_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "#exporting the pickle file\n",
    "import pickle\n",
    "pickle_out = open('pickle_files/monthly.pkl','wb')\n",
    "pickle.dump(grid_xgb,pickle_out)\n",
    "pickle_out.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([55.217323], dtype=float32)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Loading Pickle file\n",
    "model = pickle.load(open('pickle_files/monthly.pkl','rb'))\n",
    "\n",
    "#Scaling the input data\n",
    "m = scale_month.transform([[1,1110,0,1]])\n",
    "m = pd.DataFrame(m,columns=['updated_month', 'Product_type', 'Generic_Holiday','Promotion'])\n",
    "\n",
    "#predicting the output\n",
    "np.exp(model.predict(m))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>updated_month</th>\n",
       "      <th>Product_type</th>\n",
       "      <th>Promotion_applied</th>\n",
       "      <th>Generic_Holiday</th>\n",
       "      <th>no_of_units</th>\n",
       "      <th>Promotion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>239</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>75</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>1852</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>113</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36825</th>\n",
       "      <td>10</td>\n",
       "      <td>1111</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36826</th>\n",
       "      <td>10</td>\n",
       "      <td>1112</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>175</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36827</th>\n",
       "      <td>10</td>\n",
       "      <td>1113</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>209</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36828</th>\n",
       "      <td>10</td>\n",
       "      <td>1114</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>205</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36829</th>\n",
       "      <td>10</td>\n",
       "      <td>1115</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>138</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>36830 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       updated_month  Product_type  Promotion_applied  Generic_Holiday  \\\n",
       "0                  1             1                 10                0   \n",
       "1                  1             2                 10                0   \n",
       "2                  1             3                 10                0   \n",
       "3                  1             4                 10                0   \n",
       "4                  1             5                 10                0   \n",
       "...              ...           ...                ...              ...   \n",
       "36825             10          1111                  5                1   \n",
       "36826             10          1112                  5                1   \n",
       "36827             10          1113                  5                1   \n",
       "36828             10          1114                  5                1   \n",
       "36829             10          1115                  5                1   \n",
       "\n",
       "       no_of_units  Promotion  \n",
       "0              239          1  \n",
       "1               61          1  \n",
       "2               75          1  \n",
       "3             1852          1  \n",
       "4              113          1  \n",
       "...            ...        ...  \n",
       "36825           51          0  \n",
       "36826          175          0  \n",
       "36827          209          0  \n",
       "36828          205          0  \n",
       "36829          138          0  \n",
       "\n",
       "[36830 rows x 6 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building Model for Quarter dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(quarter.corr(),annot=True,cmap='RdBu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">no_of_units</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Promotion_applied</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2590.0</td>\n",
       "      <td>486.788031</td>\n",
       "      <td>947.658615</td>\n",
       "      <td>8.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>460.75</td>\n",
       "      <td>14158.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10610.0</td>\n",
       "      <td>891.313289</td>\n",
       "      <td>1365.031683</td>\n",
       "      <td>57.0</td>\n",
       "      <td>272.0</td>\n",
       "      <td>445.0</td>\n",
       "      <td>922.75</td>\n",
       "      <td>17271.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  no_of_units                                               \\\n",
       "                        count        mean          std   min    25%    50%   \n",
       "Promotion_applied                                                            \n",
       "0                      2590.0  486.788031   947.658615   8.0   86.0  213.0   \n",
       "1                     10610.0  891.313289  1365.031683  57.0  272.0  445.0   \n",
       "\n",
       "                                    \n",
       "                      75%      max  \n",
       "Promotion_applied                   \n",
       "0                  460.75  14158.0  \n",
       "1                  922.75  17271.0  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#promotion applied variable in quarter dataset\n",
    "quarter[['Promotion_applied','no_of_units']].groupby('Promotion_applied').describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████| 13200/13200 [00:08<00:00, 1485.33it/s]\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "for i in tqdm(range(0,len(quarter))):\n",
    "    if(quarter.Promotion_applied[i]<=25):\n",
    "        quarter['Promotion_applied'][i] = 0\n",
    "    else:\n",
    "        quarter['Promotion_applied'][i] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>quarter</th>\n",
       "      <th>Product_type</th>\n",
       "      <th>Promotion_applied</th>\n",
       "      <th>Generic_Holiday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000898</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001795</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002693</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.003591</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13195</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.996409</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13196</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.997307</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13197</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.998205</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13198</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.999102</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13199</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13200 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       quarter  Product_type  Promotion_applied  Generic_Holiday\n",
       "0          0.0      0.000000                0.0             0.75\n",
       "1          0.0      0.000898                0.0             0.50\n",
       "2          0.0      0.001795                0.0             0.75\n",
       "3          0.0      0.002693                0.0             0.50\n",
       "4          0.0      0.003591                0.0             0.75\n",
       "...        ...           ...                ...              ...\n",
       "13195      1.0      0.996409                0.0             0.25\n",
       "13196      1.0      0.997307                0.0             0.25\n",
       "13197      1.0      0.998205                0.0             0.25\n",
       "13198      1.0      0.999102                0.0             0.25\n",
       "13199      1.0      1.000000                0.0             0.25\n",
       "\n",
       "[13200 rows x 4 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "#creating input and output\n",
    "x = quarter.drop('no_of_units',axis=1)\n",
    "y = quarter.no_of_units\n",
    "\n",
    "#transforming the output variable\n",
    "y1 = np.round(np.log(y))\n",
    "\n",
    "#appling scaling for weekly data\n",
    "scale_quarter = MinMaxScaler()\n",
    "a = scale_quarter.fit_transform(x)\n",
    "x1 = pd.DataFrame(a,columns=x.columns)\n",
    "x1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "#exporting the pickle files\n",
    "import pickle\n",
    "file_out = open('scale_quarter.pkl','wb')\n",
    "pickle.dump(scale_quarter,file_out)\n",
    "file_out.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of Splitting\n",
      "x_train=(10560, 4), y_train=(10560,), x_test=(2640, 4),y_test=(2640,)\n"
     ]
    }
   ],
   "source": [
    "#Splitting into train and test\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(x1,y1,test_size=0.2,random_state=2)\n",
    "\n",
    "print('Shape of Splitting')\n",
    "print('x_train={}, y_train={}, x_test={},y_test={}'.format(x_train.shape,y_train.shape,x_test.shape,y_test.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9924562979819411\n",
      "r2 score :  0.1223959621208186\n"
     ]
    }
   ],
   "source": [
    "#Building the linear Regression Model\n",
    "from sklearn.metrics import r2_score, mean_squared_error\n",
    "from sklearn.linear_model import LinearRegression\n",
    "#initializing the model\n",
    "lin = LinearRegression()\n",
    "\n",
    "#fitting and predicting for test data\n",
    "pred_lin = lin.fit(x_train,y_train).predict(x_test)\n",
    "\n",
    "#calculating RMSE value\n",
    "RMSE_lin = np.sqrt(mean_squared_error(y_test,pred_lin))\n",
    "print(RMSE_lin)\n",
    "\n",
    "#calculating r2 score\n",
    "r2_lin = r2_score(y_test,pred_lin)\n",
    "print('r2 score : ',r2_lin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE value :  0.5795719871874039\n",
      "r2 score :  0.7007111060797306\n"
     ]
    }
   ],
   "source": [
    "#building Random Forest Model\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "#initializing the model\n",
    "rand = RandomForestRegressor()\n",
    "\n",
    "#fitting and predicting for test data\n",
    "pred_rand = rand.fit(x_train,y_train).predict(x_test)\n",
    "\n",
    "#calculating RMSE value\n",
    "RMSE_rand = np.sqrt(mean_squared_error(y_test,pred_rand))\n",
    "print('RMSE value : ',RMSE_rand)\n",
    "\n",
    "#calculating r2 score\n",
    "r2_rand = r2_score(y_test,pred_rand)\n",
    "print('r2 score : ',r2_rand)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 36 candidates, totalling 108 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.2min\n",
      "[Parallel(n_jobs=-1)]: Done 108 out of 108 | elapsed:  3.5min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE value :  0.5597361199764147\n",
      "r2 score :  0.7208468745443809\n",
      "Best Score : 0.6940802183505864\n",
      "Best Params : {'max_depth': 70, 'min_samples_split': 4, 'n_estimators': 250}\n"
     ]
    }
   ],
   "source": [
    "#hyper-parameters\n",
    "params = {'n_estimators': [250,300,350],\n",
    "          'max_depth': [60,70,80],\n",
    "          'min_samples_split' : [3,4,5,6],\n",
    "          }\n",
    "\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "#initializing grid\n",
    "grid_rand = GridSearchCV(rand,params,verbose=2,n_jobs=-1,cv=3)\n",
    "\n",
    "#fitting to train data\n",
    "grid_rand.fit(x_train,y_train)\n",
    "\n",
    "#fitting and predicting for test data\n",
    "pred_rand = grid_rand.predict(x_test)\n",
    "\n",
    "#calculating RMSE value\n",
    "RMSE_rand = np.sqrt(mean_squared_error(y_test,pred_rand))\n",
    "print('RMSE value : ',RMSE_rand)\n",
    "\n",
    "#calculating r2 score\n",
    "r2_rand = r2_score(y_test,pred_rand)\n",
    "print('r2 score : ',r2_rand)\n",
    "\n",
    "print('Best Score :',grid_rand.best_score_)\n",
    "print('Best Params :',grid_rand.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE value :  0.31615367772859165\n",
      "r2 score :  0.9109421297523651\n"
     ]
    }
   ],
   "source": [
    "#Building the XGBoost model\n",
    "from xgboost import XGBRegressor\n",
    "#initializing the model\n",
    "xgb = XGBRegressor(learning_rate=0.1, max_depth=10, min_child_weight=4, n_estimators=850)\n",
    "\n",
    "#fitting and predicting for test data\n",
    "pred_xgb = xgb.fit(x_train,y_train).predict(x_test)\n",
    "\n",
    "#calculating RMSE value\n",
    "RMSE_xgb = np.sqrt(mean_squared_error(y_test,pred_xgb))\n",
    "print('RMSE value : ',RMSE_xgb)\n",
    "\n",
    "#calculating r2 score\n",
    "r2_xgb = r2_score(y_test,pred_xgb)\n",
    "print('r2 score : ',r2_xgb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "#exporting the pickle file\n",
    "import pickle\n",
    "pickle_out = open('quarter.pkl','wb')\n",
    "pickle.dump(xgb,pickle_out)\n",
    "pickle_out.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 54 candidates, totalling 162 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.8min\n",
      "[Parallel(n_jobs=-1)]: Done 154 tasks      | elapsed:  8.6min\n",
      "[Parallel(n_jobs=-1)]: Done 162 out of 162 | elapsed:  9.0min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE value :  0.31615367772859165\n",
      "r2 score :  0.9109421297523651\n",
      "Best Score : 0.8869466505290756\n",
      "Best Params : {'learning_rate': 0.1, 'max_depth': 10, 'min_child_weight': 4, 'n_estimators': 850}\n"
     ]
    }
   ],
   "source": [
    "#hyper-parameters\n",
    "params = {'learning_rate': [0.01, 0.1],\n",
    "        'max_depth': [10,12,14],\n",
    "        'n_estimators' : [750,800,850],\n",
    "          'min_child_weight': [4,5,6]\n",
    "    }\n",
    "\n",
    "#initializing grid\n",
    "grid_xgb = GridSearchCV(xgb,params,verbose=2,n_jobs=-1,cv=3)\n",
    "\n",
    "#fitting to train data\n",
    "grid_xgb.fit(x_train,y_train)\n",
    "\n",
    "#fitting and predicting for test data\n",
    "pred_xgb = grid_xgb.predict(x_test)\n",
    "\n",
    "#calculating RMSE value\n",
    "RMSE_xgb = np.sqrt(mean_squared_error(y_test,pred_xgb))\n",
    "print('RMSE value : ',RMSE_xgb)\n",
    "\n",
    "#calculating r2 score\n",
    "r2_xgb = r2_score(y_test,pred_xgb)\n",
    "print('r2 score : ',r2_xgb)\n",
    "\n",
    "print('Best Score :',grid_xgb.best_score_)\n",
    "print('Best Params :',grid_xgb.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE value :  0.6868626522313636\n",
      "r2 score :  0.5796455651668777\n"
     ]
    }
   ],
   "source": [
    "#Building Decision Tree Model\n",
    "#initializing the model\n",
    "deci = DecisionTreeRegressor()\n",
    "\n",
    "#fitting and predicting for test data\n",
    "pred_deci = deci.fit(x_train,y_train).predict(x_test)\n",
    "\n",
    "#calculating RMSE value\n",
    "RMSE_deci = np.sqrt(mean_squared_error(y_test,pred_deci))\n",
    "print('RMSE value : ',RMSE_deci)\n",
    "\n",
    "#calculating r2 score\n",
    "r2_deci = r2_score(y_test,pred_deci)\n",
    "print('r2 score : ',r2_deci)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 27 candidates, totalling 81 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  58 tasks      | elapsed:    2.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE value :  0.716940167832718\n",
      "r2 score :  0.5420251226302515\n",
      "Best Score : 0.45587858529012526\n",
      "Best Params : {'max_depth': 50, 'min_samples_leaf': 10, 'min_samples_split': 20}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  74 out of  81 | elapsed:    2.3s remaining:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done  81 out of  81 | elapsed:    2.4s finished\n"
     ]
    }
   ],
   "source": [
    "#hyper-parameters\n",
    "params = {'min_samples_split': [20,30,50],\n",
    "          'max_depth': [50,100,150],\n",
    "          'min_samples_leaf': [10,20,30]\n",
    "    }\n",
    "\n",
    "#initializing grid\n",
    "grid_deci = GridSearchCV(deci,params,verbose=2,n_jobs=-1,cv=3)\n",
    "\n",
    "#fitting to train data\n",
    "grid_deci.fit(x_train,y_train)\n",
    "\n",
    "#fitting and predicting for test data\n",
    "pred_deci = grid_deci.predict(x_test)\n",
    "\n",
    "#calculating RMSE value\n",
    "RMSE_deci = np.sqrt(mean_squared_error(y_test,pred_deci))\n",
    "print('RMSE value : ',RMSE_deci)\n",
    "\n",
    "#calculating r2 score\n",
    "r2_deci = r2_score(y_test,pred_deci)\n",
    "print('r2 score : ',r2_deci)\n",
    "\n",
    "print('Best Score :',grid_deci.best_score_)\n",
    "print('Best Params :',grid_deci.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE value :  1.0076976458514455\n",
      "r2 score :  0.09523390694449285\n"
     ]
    }
   ],
   "source": [
    "#Building KNN model\n",
    "#initializing the model\n",
    "knn = KNeighborsRegressor()\n",
    "\n",
    "#fitting and predicting for test data\n",
    "pred_knn = knn.fit(x_train,y_train).predict(x_test)\n",
    "\n",
    "#calculating RMSE value\n",
    "RMSE_knn = np.sqrt(mean_squared_error(y_test,pred_knn))\n",
    "print('RMSE value : ',RMSE_knn)\n",
    "\n",
    "#calculating r2 score\n",
    "r2_knn = r2_score(y_test,pred_knn)\n",
    "print('r2 score : ',r2_knn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  30 out of  30 | elapsed:    4.7s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE value :  0.9403601327925056\n",
      "r2 score :  0.212112435444881\n",
      "Best Score : 0.1911593897231766\n",
      "Best Params : {'n_neighbors': 48}\n"
     ]
    }
   ],
   "source": [
    "#hyper-parameters\n",
    "params = {'n_neighbors': range(30,50,2)\n",
    "    }\n",
    "\n",
    "#initializing grid\n",
    "grid_knn = GridSearchCV(knn,params,verbose=2,n_jobs=-1,cv=3)\n",
    "\n",
    "#fitting to train data\n",
    "grid_knn.fit(x_train,y_train)\n",
    "\n",
    "#fitting and predicting for test data\n",
    "pred_knn = grid_knn.predict(x_test)\n",
    "\n",
    "#calculating RMSE value\n",
    "RMSE_knn = np.sqrt(mean_squared_error(y_test,pred_knn))\n",
    "print('RMSE value : ',RMSE_knn)\n",
    "\n",
    "#calculating r2 score\n",
    "r2_knn = r2_score(y_test,pred_knn)\n",
    "print('r2 score : ',r2_knn)\n",
    "\n",
    "print('Best Score :',grid_knn.best_score_)\n",
    "print('Best Params :',grid_knn.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RMSE</th>\n",
       "      <th>r2_Score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algorithm</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Linear Reg</th>\n",
       "      <td>0.992456</td>\n",
       "      <td>0.122396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNN</th>\n",
       "      <td>1.007698</td>\n",
       "      <td>0.095234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.686863</td>\n",
       "      <td>0.579646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>0.559736</td>\n",
       "      <td>0.720847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XGBoost</th>\n",
       "      <td>0.316154</td>\n",
       "      <td>0.910942</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   RMSE  r2_Score\n",
       "Algorithm                        \n",
       "Linear Reg     0.992456  0.122396\n",
       "KNN            1.007698  0.095234\n",
       "Decision Tree  0.686863  0.579646\n",
       "Random Forest  0.559736  0.720847\n",
       "XGBoost        0.316154  0.910942"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#consolidating the results\n",
    "Algorithm = ['Linear Reg','KNN','Decision Tree','Random Forest','XGBoost']\n",
    "RMSE = [RMSE_lin,RMSE_knn,RMSE_deci,RMSE_rand,RMSE_xgb]\n",
    "r2_score = [r2_lin,r2_knn,r2_deci,r2_rand,r2_xgb]\n",
    "quarter_result = pd.DataFrame({'Algorithm':Algorithm,'RMSE':RMSE,'r2_Score':r2_score})\n",
    "quarter_result = quarter_result.set_index('Algorithm')\n",
    "quarter_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "#exporting the pickle file\n",
    "import pickle\n",
    "pickle_out = open('pickle_files/quarter.pkl','wb')\n",
    "pickle.dump(grid_xgb,pickle_out)\n",
    "pickle_out.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([344.73492], dtype=float32)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Loading Pickle file\n",
    "model = pickle.load(open('pickle_files/quarter.pkl','rb'))\n",
    "\n",
    "#Scaling the input data\n",
    "m = scale_quarter.transform([[1,5,1,1]])\n",
    "m = pd.DataFrame(m,columns=quarter.columns[:-1])\n",
    "\n",
    "#predicting the output\n",
    "np.exp(model.predict(m))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "quarter.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "all = pd.DataFrame(columns=['quarter', 'Product_type', 'Promotion_applied', 'Generic_Holiday'])\n",
    "all.Product_type = range(1,1116,1); all.Promotion_applied = 0 ; all.quarter = 1; all.Generic_Holiday = 0\n",
    "all = pd.DataFrame(scale_quarter.transform(all),columns=all.columns)\n",
    "np.exp(model.predict(all))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "name": "Inventry Management Model.ipynb",
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